diff --git a/src/renderer/src/i18n/locales/en-us.json b/src/renderer/src/i18n/locales/en-us.json
index dae4ed6ca5..3d8ab74b96 100644
--- a/src/renderer/src/i18n/locales/en-us.json
+++ b/src/renderer/src/i18n/locales/en-us.json
@@ -705,6 +705,7 @@
       "rerank_model_tooltip": "Click the Manage button in Settings -> Model Services to add.",
       "search": "Search models...",
       "stream_output": "Stream output",
+      "enable_tool_use": "Enable Tool Use",
       "type": {
         "embedding": "Embedding",
         "free": "Free",
diff --git a/src/renderer/src/i18n/locales/ja-jp.json b/src/renderer/src/i18n/locales/ja-jp.json
index 046d2ff589..f1b12358d8 100644
--- a/src/renderer/src/i18n/locales/ja-jp.json
+++ b/src/renderer/src/i18n/locales/ja-jp.json
@@ -705,6 +705,7 @@
       "rerank_model_tooltip": "設定->モデルサービスに移動し、管理ボタンをクリックして追加します。",
       "search": "モデルを検索...",
       "stream_output": "ストリーム出力",
+      "enable_tool_use": "ツール呼び出し",
       "type": {
         "embedding": "埋め込み",
         "free": "無料",
diff --git a/src/renderer/src/i18n/locales/ru-ru.json b/src/renderer/src/i18n/locales/ru-ru.json
index 41242b74c3..2692e1c270 100644
--- a/src/renderer/src/i18n/locales/ru-ru.json
+++ b/src/renderer/src/i18n/locales/ru-ru.json
@@ -705,6 +705,7 @@
       "rerank_model_tooltip": "В настройках -> Служба модели нажмите кнопку \"Управление\", чтобы добавить.",
       "search": "Поиск моделей...",
       "stream_output": "Потоковый вывод",
+      "enable_tool_use": "Вызов инструмента",
       "type": {
         "embedding": "Встраиваемые",
         "free": "Бесплатные",
diff --git a/src/renderer/src/i18n/locales/zh-cn.json b/src/renderer/src/i18n/locales/zh-cn.json
index a57c106b49..e68409a642 100644
--- a/src/renderer/src/i18n/locales/zh-cn.json
+++ b/src/renderer/src/i18n/locales/zh-cn.json
@@ -705,6 +705,7 @@
       "rerank_model_tooltip": "在设置->模型服务中点击管理按钮添加",
       "search": "搜索模型...",
       "stream_output": "流式输出",
+      "enable_tool_use": "工具调用",
       "type": {
         "embedding": "嵌入",
         "free": "免费",
diff --git a/src/renderer/src/i18n/locales/zh-tw.json b/src/renderer/src/i18n/locales/zh-tw.json
index d91d8fb257..893a7b75a1 100644
--- a/src/renderer/src/i18n/locales/zh-tw.json
+++ b/src/renderer/src/i18n/locales/zh-tw.json
@@ -705,6 +705,7 @@
       "rerank_model_tooltip": "在設定->模型服務中點擊管理按鈕添加",
       "search": "搜尋模型...",
       "stream_output": "串流輸出",
+      "enable_tool_use": "工具調用",
       "type": {
         "embedding": "嵌入",
         "free": "免費",
diff --git a/src/renderer/src/pages/home/Messages/MessageTools.tsx b/src/renderer/src/pages/home/Messages/MessageTools.tsx
index 495c56cf10..b281e40642 100644
--- a/src/renderer/src/pages/home/Messages/MessageTools.tsx
+++ b/src/renderer/src/pages/home/Messages/MessageTools.tsx
@@ -67,7 +67,7 @@ const MessageTools: FC<Props> = ({ blocks }) => {
     const isDone = status === 'done'
     const hasError = isDone && response?.isError === true
     const result = {
-      params: tool.inputSchema,
+      params: toolResponse.arguments,
       response: toolResponse.response
     }
 
diff --git a/src/renderer/src/pages/home/Tabs/SettingsTab.tsx b/src/renderer/src/pages/home/Tabs/SettingsTab.tsx
index 9a21b1c808..0c8972db6d 100644
--- a/src/renderer/src/pages/home/Tabs/SettingsTab.tsx
+++ b/src/renderer/src/pages/home/Tabs/SettingsTab.tsx
@@ -70,6 +70,7 @@ const SettingsTab: FC<Props> = (props) => {
   const [maxTokens, setMaxTokens] = useState(assistant?.settings?.maxTokens ?? 0)
   const [fontSizeValue, setFontSizeValue] = useState(fontSize)
   const [streamOutput, setStreamOutput] = useState(assistant?.settings?.streamOutput ?? true)
+  const [enableToolUse, setEnableToolUse] = useState(assistant?.settings?.enableToolUse ?? false)
   const { t } = useTranslation()
 
   const dispatch = useAppDispatch()
@@ -222,6 +223,18 @@ const SettingsTab: FC<Props> = (props) => {
           />
         </SettingRow>
         <SettingDivider />
+        <SettingRow>
+          <SettingRowTitleSmall>{t('models.enable_tool_use')}</SettingRowTitleSmall>
+          <Switch
+            size="small"
+            checked={enableToolUse}
+            onChange={(checked) => {
+              setEnableToolUse(checked)
+              updateAssistantSettings({ enableToolUse: checked })
+            }}
+          />
+        </SettingRow>
+        <SettingDivider />
         <Row align="middle" justify="space-between" style={{ marginBottom: 10 }}>
           <HStack alignItems="center">
             <Label>{t('chat.settings.max_tokens')}</Label>
diff --git a/src/renderer/src/pages/settings/AssistantSettings/AssistantModelSettings.tsx b/src/renderer/src/pages/settings/AssistantSettings/AssistantModelSettings.tsx
index 31075ebbb7..9ea4559b47 100644
--- a/src/renderer/src/pages/settings/AssistantSettings/AssistantModelSettings.tsx
+++ b/src/renderer/src/pages/settings/AssistantSettings/AssistantModelSettings.tsx
@@ -24,6 +24,7 @@ const AssistantModelSettings: FC<Props> = ({ assistant, updateAssistant, updateA
   const [enableMaxTokens, setEnableMaxTokens] = useState(assistant?.settings?.enableMaxTokens ?? false)
   const [maxTokens, setMaxTokens] = useState(assistant?.settings?.maxTokens ?? 0)
   const [streamOutput, setStreamOutput] = useState(assistant?.settings?.streamOutput ?? true)
+  const [enableToolUse, setEnableToolUse] = useState(assistant?.settings?.enableToolUse ?? false)
   const [defaultModel, setDefaultModel] = useState(assistant?.defaultModel)
   const [topP, setTopP] = useState(assistant?.settings?.topP ?? 1)
   const [customParameters, setCustomParameters] = useState<AssistantSettingCustomParameters[]>(
@@ -377,6 +378,18 @@ const AssistantModelSettings: FC<Props> = ({ assistant, updateAssistant, updateA
         />
       </SettingRow>
       <Divider style={{ margin: '10px 0' }} />
+      <SettingRow style={{ minHeight: 30 }}>
+        <Label>{t('models.enable_tool_use')}</Label>
+        <Switch
+          size="small"
+          checked={enableToolUse}
+          onChange={(checked) => {
+            setEnableToolUse(checked)
+            updateAssistantSettings({ enableToolUse: checked })
+          }}
+        />
+      </SettingRow>
+      <Divider style={{ margin: '10px 0' }} />
       <SettingRow style={{ minHeight: 30 }}>
         <Label>{t('models.custom_parameters')}</Label>
         <Button icon={<PlusOutlined />} onClick={onAddCustomParameter}>
diff --git a/src/renderer/src/providers/AiProvider/AihubmixProvider.ts b/src/renderer/src/providers/AiProvider/AihubmixProvider.ts
index 8961a050d4..83b5377b5c 100644
--- a/src/renderer/src/providers/AiProvider/AihubmixProvider.ts
+++ b/src/renderer/src/providers/AiProvider/AihubmixProvider.ts
@@ -1,6 +1,6 @@
 import { isOpenAILLMModel } from '@renderer/config/models'
 import { getDefaultModel } from '@renderer/services/AssistantService'
-import { Assistant, Model, Provider, Suggestion } from '@renderer/types'
+import { Assistant, MCPCallToolResponse, MCPTool, MCPToolResponse, Model, Provider, Suggestion } from '@renderer/types'
 import { Message } from '@renderer/types/newMessage'
 import OpenAI from 'openai'
 
@@ -18,6 +18,7 @@ import OpenAIProvider from './OpenAIProvider'
 export default class AihubmixProvider extends BaseProvider {
   private providers: Map<string, BaseProvider> = new Map()
   private defaultProvider: BaseProvider
+  private currentProvider: BaseProvider
 
   constructor(provider: Provider) {
     super(provider)
@@ -30,6 +31,7 @@ export default class AihubmixProvider extends BaseProvider {
 
     // 设置默认提供商
     this.defaultProvider = this.providers.get('default')!
+    this.currentProvider = this.defaultProvider
   }
 
   /**
@@ -70,7 +72,8 @@ export default class AihubmixProvider extends BaseProvider {
 
   public async completions(params: CompletionsParams): Promise<void> {
     const model = params.assistant.model
-    return this.getProvider(model!).completions(params)
+    this.currentProvider = this.getProvider(model!)
+    return this.currentProvider.completions(params)
   }
 
   public async translate(
@@ -100,4 +103,12 @@ export default class AihubmixProvider extends BaseProvider {
   public async getEmbeddingDimensions(model: Model): Promise<number> {
     return this.getProvider(model).getEmbeddingDimensions(model)
   }
+
+  public convertMcpTools<T>(mcpTools: MCPTool[]) {
+    return this.currentProvider.convertMcpTools(mcpTools) as T[]
+  }
+
+  public mcpToolCallResponseToMessage(mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model) {
+    return this.currentProvider.mcpToolCallResponseToMessage(mcpToolResponse, resp, model)
+  }
 }
diff --git a/src/renderer/src/providers/AiProvider/AnthropicProvider.ts b/src/renderer/src/providers/AiProvider/AnthropicProvider.ts
index d113d7f532..1f9fdfbe7e 100644
--- a/src/renderer/src/providers/AiProvider/AnthropicProvider.ts
+++ b/src/renderer/src/providers/AiProvider/AnthropicProvider.ts
@@ -1,15 +1,19 @@
 import Anthropic from '@anthropic-ai/sdk'
 import {
+  Base64ImageSource,
+  ImageBlockParam,
   MessageCreateParamsNonStreaming,
   MessageParam,
   TextBlockParam,
+  ToolResultBlockParam,
   ToolUnion,
+  ToolUseBlock,
   WebSearchResultBlock,
   WebSearchTool20250305,
   WebSearchToolResultError
 } from '@anthropic-ai/sdk/resources'
 import { DEFAULT_MAX_TOKENS } from '@renderer/config/constant'
-import { isReasoningModel, isVisionModel, isWebSearchModel } from '@renderer/config/models'
+import { isReasoningModel, isWebSearchModel } from '@renderer/config/models'
 import { getStoreSetting } from '@renderer/hooks/useSettings'
 import i18n from '@renderer/i18n'
 import { getAssistantSettings, getDefaultModel, getTopNamingModel } from '@renderer/services/AssistantService'
@@ -23,16 +27,24 @@ import {
   Assistant,
   EFFORT_RATIO,
   FileTypes,
+  MCPCallToolResponse,
+  MCPTool,
   MCPToolResponse,
   Model,
   Provider,
   Suggestion,
+  ToolCallResponse,
   WebSearchSource
 } from '@renderer/types'
 import { ChunkType } from '@renderer/types/chunk'
 import type { Message } from '@renderer/types/newMessage'
 import { removeSpecialCharactersForTopicName } from '@renderer/utils'
-import { mcpToolCallResponseToAnthropicMessage, parseAndCallTools } from '@renderer/utils/mcp-tools'
+import {
+  anthropicToolUseToMcpTool,
+  mcpToolCallResponseToAnthropicMessage,
+  mcpToolsToAnthropicTools,
+  parseAndCallTools
+} from '@renderer/utils/mcp-tools'
 import { findFileBlocks, findImageBlocks, getMainTextContent } from '@renderer/utils/messageUtils/find'
 import { buildSystemPrompt } from '@renderer/utils/prompt'
 import { first, flatten, sum, takeRight } from 'lodash'
@@ -199,7 +211,7 @@ export default class AnthropicProvider extends BaseProvider {
   public async completions({ messages, assistant, mcpTools, onChunk, onFilterMessages }: CompletionsParams) {
     const defaultModel = getDefaultModel()
     const model = assistant.model || defaultModel
-    const { contextCount, maxTokens, streamOutput } = getAssistantSettings(assistant)
+    const { contextCount, maxTokens, streamOutput, enableToolUse } = getAssistantSettings(assistant)
 
     const userMessagesParams: MessageParam[] = []
 
@@ -215,10 +227,16 @@ export default class AnthropicProvider extends BaseProvider {
 
     const userMessages = flatten(userMessagesParams)
     const lastUserMessage = _messages.findLast((m) => m.role === 'user')
-    // const tools = mcpTools ? mcpToolsToAnthropicTools(mcpTools) : undefined
 
     let systemPrompt = assistant.prompt
-    if (mcpTools && mcpTools.length > 0) {
+
+    const { tools } = this.setupToolsConfig<ToolUnion>({
+      model,
+      mcpTools,
+      enableToolUse
+    })
+
+    if (this.useSystemPromptForTools && mcpTools && mcpTools.length) {
       systemPrompt = buildSystemPrompt(systemPrompt, mcpTools)
     }
 
@@ -232,8 +250,6 @@ export default class AnthropicProvider extends BaseProvider {
 
     const isEnabledBuiltinWebSearch = assistant.enableWebSearch
 
-    const tools: ToolUnion[] = []
-
     if (isEnabledBuiltinWebSearch) {
       const webSearchTool = await this.getWebSearchParams(model)
       if (webSearchTool) {
@@ -244,7 +260,6 @@ export default class AnthropicProvider extends BaseProvider {
     const body: MessageCreateParamsNonStreaming = {
       model: model.id,
       messages: userMessages,
-      // tools: isEmpty(tools) ? undefined : tools,
       max_tokens: maxTokens || DEFAULT_MAX_TOKENS,
       temperature: this.getTemperature(assistant, model),
       top_p: this.getTopP(assistant, model),
@@ -303,7 +318,7 @@ export default class AnthropicProvider extends BaseProvider {
     const processStream = (body: MessageCreateParamsNonStreaming, idx: number) => {
       return new Promise<void>((resolve, reject) => {
         // 等待接口返回流
-        onChunk({ type: ChunkType.LLM_RESPONSE_CREATED })
+        const toolCalls: ToolUseBlock[] = []
         let hasThinkingContent = false
         this.sdk.messages
           .stream({ ...body, stream: true }, { signal, timeout: 5 * 60 * 1000 })
@@ -380,30 +395,70 @@ export default class AnthropicProvider extends BaseProvider {
             })
             thinking_content += thinking
           })
+          .on('contentBlock', (content) => {
+            if (content.type === 'tool_use') {
+              toolCalls.push(content)
+            }
+          })
           .on('finalMessage', async (message) => {
+            const toolResults: Awaited<ReturnType<typeof parseAndCallTools>> = []
+            // tool call
+            if (toolCalls.length > 0) {
+              const mcpToolResponses = toolCalls
+                .map((toolCall) => {
+                  const mcpTool = anthropicToolUseToMcpTool(mcpTools, toolCall)
+                  if (!mcpTool) {
+                    return undefined
+                  }
+                  return {
+                    id: toolCall.id,
+                    toolCallId: toolCall.id,
+                    tool: mcpTool,
+                    arguments: toolCall.input as Record<string, unknown>,
+                    status: 'pending'
+                  } as ToolCallResponse
+                })
+                .filter((t) => typeof t !== 'undefined')
+              toolResults.push(
+                ...(await parseAndCallTools(
+                  mcpToolResponses,
+                  toolResponses,
+                  onChunk,
+                  this.mcpToolCallResponseToMessage,
+                  model,
+                  mcpTools
+                ))
+              )
+            }
+
+            // tool use
             const content = message.content[0]
             if (content && content.type === 'text') {
               onChunk({ type: ChunkType.TEXT_COMPLETE, text: content.text })
-              const toolResults = await parseAndCallTools(
-                content.text,
-                toolResponses,
-                onChunk,
-                idx,
-                mcpToolCallResponseToAnthropicMessage,
-                mcpTools,
-                isVisionModel(model)
+              toolResults.push(
+                ...(await parseAndCallTools(
+                  content.text,
+                  toolResponses,
+                  onChunk,
+                  this.mcpToolCallResponseToMessage,
+                  model,
+                  mcpTools
+                ))
               )
-              if (toolResults.length > 0) {
-                userMessages.push({
-                  role: message.role,
-                  content: message.content
-                })
+            }
 
-                toolResults.forEach((ts) => userMessages.push(ts as MessageParam))
-                const newBody = body
-                newBody.messages = userMessages
-                await processStream(newBody, idx + 1)
-              }
+            userMessages.push({
+              role: message.role,
+              content: message.content
+            })
+
+            if (toolResults.length > 0) {
+              toolResults.forEach((ts) => userMessages.push(ts as MessageParam))
+              const newBody = body
+              newBody.messages = userMessages
+
+              onChunk({ type: ChunkType.LLM_RESPONSE_CREATED })
+              await processStream(newBody, idx + 1)
             }
 
             const time_completion_millsec = new Date().getTime() - start_time_millsec
@@ -434,7 +489,7 @@ export default class AnthropicProvider extends BaseProvider {
           })
       })
     }
-
+    onChunk({ type: ChunkType.LLM_RESPONSE_CREATED })
     await processStream(body, 0).finally(cleanup)
   }
 
@@ -683,4 +738,47 @@ export default class AnthropicProvider extends BaseProvider {
   public async getEmbeddingDimensions(): Promise<number> {
     return 0
   }
+
+  public convertMcpTools<T>(mcpTools: MCPTool[]): T[] {
+    return mcpToolsToAnthropicTools(mcpTools) as T[]
+  }
+
+  public mcpToolCallResponseToMessage = (mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model) => {
+    if ('toolUseId' in mcpToolResponse && mcpToolResponse.toolUseId) {
+      return mcpToolCallResponseToAnthropicMessage(mcpToolResponse, resp, model)
+    } else if ('toolCallId' in mcpToolResponse) {
+      return {
+        role: 'user',
+        content: [
+          {
+            type: 'tool_result',
+            tool_use_id: mcpToolResponse.toolCallId!,
+            content: resp.content
+              .map((item) => {
+                if (item.type === 'text') {
+                  return {
+                    type: 'text',
+                    text: item.text || ''
+                  } satisfies TextBlockParam
+                }
+                if (item.type === 'image') {
+                  return {
+                    type: 'image',
+                    source: {
+                      data: item.data || '',
+                      media_type: (item.mimeType || 'image/png') as Base64ImageSource['media_type'],
+                      type: 'base64'
+                    }
+                  } satisfies ImageBlockParam
+                }
+                return
+              })
+              .filter((n) => typeof n !== 'undefined'),
+            is_error: resp.isError
+          } satisfies ToolResultBlockParam
+        ]
+      }
+    }
+    return
+  }
 }
diff --git a/src/renderer/src/providers/AiProvider/BaseProvider.ts b/src/renderer/src/providers/AiProvider/BaseProvider.ts
index f39f09b20c..5af351aa57 100644
--- a/src/renderer/src/providers/AiProvider/BaseProvider.ts
+++ b/src/renderer/src/providers/AiProvider/BaseProvider.ts
@@ -1,9 +1,13 @@
+import { isFunctionCallingModel } from '@renderer/config/models'
 import { REFERENCE_PROMPT } from '@renderer/config/prompts'
 import { getLMStudioKeepAliveTime } from '@renderer/hooks/useLMStudio'
 import type {
   Assistant,
   GenerateImageParams,
   KnowledgeReference,
+  MCPCallToolResponse,
+  MCPTool,
+  MCPToolResponse,
   Model,
   Provider,
   Suggestion,
@@ -22,10 +26,15 @@ import type OpenAI from 'openai'
 import type { CompletionsParams } from '.'
 
 export default abstract class BaseProvider {
+  // Threshold for determining whether to use system prompt for tools
+  private static readonly SYSTEM_PROMPT_THRESHOLD: number = 128
+
   protected provider: Provider
   protected host: string
   protected apiKey: string
 
+  protected useSystemPromptForTools: boolean = true
+
   constructor(provider: Provider) {
     this.provider = provider
     this.host = this.getBaseURL()
@@ -47,6 +56,12 @@ export default abstract class BaseProvider {
   abstract generateImage(params: GenerateImageParams): Promise<string[]>
   abstract generateImageByChat({ messages, assistant, onChunk, onFilterMessages }: CompletionsParams): Promise<void>
   abstract getEmbeddingDimensions(model: Model): Promise<number>
+  public abstract convertMcpTools<T>(mcpTools: MCPTool[]): T[]
+  public abstract mcpToolCallResponseToMessage(
+    mcpToolResponse: MCPToolResponse,
+    resp: MCPCallToolResponse,
+    model: Model
+  ): any
 
   public getBaseURL(): string {
     const host = this.provider.apiHost
@@ -229,4 +244,31 @@ export default abstract class BaseProvider {
       cleanup
     }
   }
+
+  // Setup tools configuration based on provided parameters
+  protected setupToolsConfig<T>(params: { mcpTools?: MCPTool[]; model: Model; enableToolUse?: boolean }): {
+    tools: T[]
+  } {
+    const { mcpTools, model, enableToolUse } = params
+    let tools: T[] = []
+
+    // If there are no tools, return an empty array
+    if (!mcpTools?.length) {
+      return { tools }
+    }
+
+    // If the number of tools exceeds the threshold, use the system prompt
+    if (mcpTools.length > BaseProvider.SYSTEM_PROMPT_THRESHOLD) {
+      this.useSystemPromptForTools = true
+      return { tools }
+    }
+
+    // If the model supports function calling and tool usage is enabled
+    if (isFunctionCallingModel(model) && enableToolUse) {
+      tools = this.convertMcpTools<T>(mcpTools)
+      this.useSystemPromptForTools = false
+    }
+
+    return { tools }
+  }
 }
diff --git a/src/renderer/src/providers/AiProvider/GeminiProvider.ts b/src/renderer/src/providers/AiProvider/GeminiProvider.ts
index 57a50cdca4..9ccef9b28d 100644
--- a/src/renderer/src/providers/AiProvider/GeminiProvider.ts
+++ b/src/renderer/src/providers/AiProvider/GeminiProvider.ts
@@ -1,6 +1,7 @@
 import {
   Content,
   File,
+  FunctionCall,
   GenerateContentConfig,
   GenerateContentResponse,
   GoogleGenAI,
@@ -11,8 +12,9 @@ import {
   PartUnion,
   SafetySetting,
   ThinkingConfig,
-  ToolListUnion
+  Tool
 } from '@google/genai'
+import { nanoid } from '@reduxjs/toolkit'
 import {
   findTokenLimit,
   isGeminiReasoningModel,
@@ -35,17 +37,25 @@ import {
   EFFORT_RATIO,
   FileType,
   FileTypes,
+  MCPCallToolResponse,
+  MCPTool,
   MCPToolResponse,
   Model,
   Provider,
   Suggestion,
+  ToolCallResponse,
   Usage,
   WebSearchSource
 } from '@renderer/types'
 import { BlockCompleteChunk, Chunk, ChunkType, LLMWebSearchCompleteChunk } from '@renderer/types/chunk'
 import type { Message, Response } from '@renderer/types/newMessage'
 import { removeSpecialCharactersForTopicName } from '@renderer/utils'
-import { mcpToolCallResponseToGeminiMessage, parseAndCallTools } from '@renderer/utils/mcp-tools'
+import {
+  geminiFunctionCallToMcpTool,
+  mcpToolCallResponseToGeminiMessage,
+  mcpToolsToGeminiTools,
+  parseAndCallTools
+} from '@renderer/utils/mcp-tools'
 import { findFileBlocks, findImageBlocks, getMainTextContent } from '@renderer/utils/messageUtils/find'
 import { buildSystemPrompt } from '@renderer/utils/prompt'
 import { MB } from '@shared/config/constant'
@@ -263,7 +273,7 @@ export default class GeminiProvider extends BaseProvider {
   }: CompletionsParams): Promise<void> {
     const defaultModel = getDefaultModel()
     const model = assistant.model || defaultModel
-    const { contextCount, maxTokens, streamOutput } = getAssistantSettings(assistant)
+    const { contextCount, maxTokens, streamOutput, enableToolUse } = getAssistantSettings(assistant)
 
     const userMessages = filterUserRoleStartMessages(
       filterEmptyMessages(filterContextMessages(takeRight(messages, contextCount + 2)))
@@ -280,12 +290,16 @@ export default class GeminiProvider extends BaseProvider {
 
     let systemInstruction = assistant.prompt
 
-    if (mcpTools && mcpTools.length > 0) {
+    const { tools } = this.setupToolsConfig<Tool>({
+      mcpTools,
+      model,
+      enableToolUse
+    })
+
+    if (this.useSystemPromptForTools) {
       systemInstruction = buildSystemPrompt(assistant.prompt || '', mcpTools)
     }
 
-    // const tools = mcpToolsToGeminiTools(mcpTools)
-    const tools: ToolListUnion = []
     const toolResponses: MCPToolResponse[] = []
 
     if (assistant.enableWebSearch && isWebSearchModel(model)) {
@@ -351,6 +365,224 @@ export default class GeminiProvider extends BaseProvider {
 
     const { cleanup, abortController } = this.createAbortController(userLastMessage?.id, true)
 
+    const processToolResults = async (toolResults: Awaited<ReturnType<typeof parseAndCallTools>>, idx: number) => {
+      if (toolResults.length === 0) return
+      const newChat = this.sdk.chats.create({
+        model: model.id,
+        config: generateContentConfig,
+        history: history as Content[]
+      })
+
+      const newStream = await newChat.sendMessageStream({
+        message: flatten(toolResults.map((ts) => (ts as Content).parts)) as PartUnion,
+        config: {
+          ...generateContentConfig,
+          abortSignal: abortController.signal
+        }
+      })
+      await processStream(newStream, idx + 1)
+    }
+
+    const processToolCalls = async (toolCalls: FunctionCall[]) => {
+      const mcpToolResponses: ToolCallResponse[] = toolCalls
+        .map((toolCall) => {
+          const mcpTool = geminiFunctionCallToMcpTool(mcpTools, toolCall)
+          if (!mcpTool) return undefined
+
+          const parsedArgs = (() => {
+            try {
+              return typeof toolCall.args === 'string' ? JSON.parse(toolCall.args) : toolCall.args
+            } catch {
+              return toolCall.args
+            }
+          })()
+
+          return {
+            id: toolCall.id || nanoid(),
+            toolCallId: toolCall.id,
+            tool: mcpTool,
+            arguments: parsedArgs,
+            status: 'pending'
+          } as ToolCallResponse
+        })
+        .filter((t): t is ToolCallResponse => typeof t !== 'undefined')
+
+      return await parseAndCallTools(
+        mcpToolResponses,
+        toolResponses,
+        onChunk,
+        this.mcpToolCallResponseToMessage,
+        model,
+        mcpTools
+      )
+    }
+
+    const processToolUses = async (content: string) => {
+      return await parseAndCallTools(
+        content,
+        toolResponses,
+        onChunk,
+        this.mcpToolCallResponseToMessage,
+        model,
+        mcpTools
+      )
+    }
+
+    const processStream = async (
+      stream: AsyncGenerator<GenerateContentResponse> | GenerateContentResponse,
+      idx: number
+    ) => {
+      history.push(messageContents)
+
+      let functionCalls: FunctionCall[] = []
+
+      if (stream instanceof GenerateContentResponse) {
+        let content = ''
+        const time_completion_millsec = new Date().getTime() - start_time_millsec
+
+        const toolResults: Awaited<ReturnType<typeof parseAndCallTools>> = []
+        if (stream.text?.length) {
+          toolResults.push(...(await processToolUses(stream.text)))
+        }
+        stream.candidates?.forEach((candidate) => {
+          if (candidate.content) {
+            history.push(candidate.content)
+
+            candidate.content.parts?.forEach((part) => {
+              if (part.functionCall) {
+                functionCalls.push(part.functionCall)
+              }
+              if (part.text) {
+                content += part.text
+                onChunk({ type: ChunkType.TEXT_DELTA, text: part.text })
+              }
+            })
+          }
+        })
+        if (content.length) {
+          onChunk({ type: ChunkType.TEXT_COMPLETE, text: content })
+        }
+        if (functionCalls.length) {
+          toolResults.push(...(await processToolCalls(functionCalls)))
+        }
+        if (stream.text?.length) {
+          toolResults.push(...(await processToolUses(stream.text)))
+        }
+        if (toolResults.length) {
+          await processToolResults(toolResults, idx)
+        }
+        onChunk({
+          type: ChunkType.BLOCK_COMPLETE,
+          response: {
+            text: stream.text,
+            usage: {
+              prompt_tokens: stream.usageMetadata?.promptTokenCount || 0,
+              thoughts_tokens: stream.usageMetadata?.thoughtsTokenCount || 0,
+              completion_tokens: stream.usageMetadata?.candidatesTokenCount || 0,
+              total_tokens: stream.usageMetadata?.totalTokenCount || 0
+            },
+            metrics: {
+              completion_tokens: stream.usageMetadata?.candidatesTokenCount,
+              time_completion_millsec,
+              time_first_token_millsec: 0
+            },
+            webSearch: {
+              results: stream.candidates?.[0]?.groundingMetadata,
+              source: 'gemini'
+            }
+          } as Response
+        } as BlockCompleteChunk)
+      } else {
+        let content = ''
+        let final_time_completion_millsec = 0
+        let lastUsage: Usage | undefined = undefined
+        for await (const chunk of stream) {
+          if (window.keyv.get(EVENT_NAMES.CHAT_COMPLETION_PAUSED)) break
+
+          // --- Calculate Metrics ---
+          if (time_first_token_millsec == 0 && chunk.text !== undefined) {
+            // Update based on text arrival
+            time_first_token_millsec = new Date().getTime() - start_time_millsec
+          }
+
+          // 1. Text Content
+          if (chunk.text !== undefined) {
+            content += chunk.text
+            onChunk({ type: ChunkType.TEXT_DELTA, text: chunk.text })
+          }
+
+          // 2. Usage Data
+          if (chunk.usageMetadata) {
+            lastUsage = {
+              prompt_tokens: chunk.usageMetadata.promptTokenCount || 0,
+              completion_tokens: chunk.usageMetadata.candidatesTokenCount || 0,
+              total_tokens: chunk.usageMetadata.totalTokenCount || 0
+            }
+            final_time_completion_millsec = new Date().getTime() - start_time_millsec
+          }
+
+          // 4. Image Generation
+          const generateImage = this.processGeminiImageResponse(chunk, onChunk)
+          if (generateImage?.images?.length) {
+            onChunk({ type: ChunkType.IMAGE_COMPLETE, image: generateImage })
+          }
+
+          if (chunk.candidates?.[0]?.finishReason) {
+            if (chunk.text) {
+              onChunk({ type: ChunkType.TEXT_COMPLETE, text: content })
+            }
+            if (chunk.candidates?.[0]?.groundingMetadata) {
+              // 3. Grounding/Search Metadata
+              const groundingMetadata = chunk.candidates?.[0]?.groundingMetadata
+              onChunk({
+                type: ChunkType.LLM_WEB_SEARCH_COMPLETE,
+                llm_web_search: {
+                  results: groundingMetadata,
+                  source: WebSearchSource.GEMINI
+                }
+              } as LLMWebSearchCompleteChunk)
+            }
+            if (chunk.functionCalls) {
+              chunk.candidates?.forEach((candidate) => {
+                if (candidate.content) {
+                  history.push(candidate.content)
+                }
+              })
+              functionCalls = functionCalls.concat(chunk.functionCalls)
+            }
+
+            onChunk({
+              type: ChunkType.BLOCK_COMPLETE,
+              response: {
+                metrics: {
+                  completion_tokens: lastUsage?.completion_tokens,
+                  time_completion_millsec: final_time_completion_millsec,
+                  time_first_token_millsec
+                },
+                usage: lastUsage
+              }
+            })
+          }
+
+          // --- End Incremental onChunk calls ---
+
+          // Call processToolUses AFTER potentially processing text content in this chunk
+          // This assumes tools might be specified within the text stream
+          // Note: parseAndCallTools inside should handle its own onChunk for tool responses
+          let toolResults: Awaited<ReturnType<typeof parseAndCallTools>> = []
+          if (functionCalls.length) {
+            toolResults = await processToolCalls(functionCalls)
+          }
+          if (content.length) {
+            toolResults = toolResults.concat(await processToolUses(content))
+          }
+          if (toolResults.length) {
+            await processToolResults(toolResults, idx)
+          }
+        }
+      }
+    }
+
     if (!streamOutput) {
       const response = await chat.sendMessage({
         message: messageContents as PartUnion,
@@ -359,32 +591,10 @@ export default class GeminiProvider extends BaseProvider {
           abortSignal: abortController.signal
         }
       })
-      const time_completion_millsec = new Date().getTime() - start_time_millsec
-      onChunk({
-        type: ChunkType.BLOCK_COMPLETE,
-        response: {
-          text: response.text,
-          usage: {
-            prompt_tokens: response.usageMetadata?.promptTokenCount || 0,
-            thoughts_tokens: response.usageMetadata?.thoughtsTokenCount || 0,
-            completion_tokens: response.usageMetadata?.candidatesTokenCount || 0,
-            total_tokens: response.usageMetadata?.totalTokenCount || 0
-          },
-          metrics: {
-            completion_tokens: response.usageMetadata?.candidatesTokenCount,
-            time_completion_millsec,
-            time_first_token_millsec: 0
-          },
-          webSearch: {
-            results: response.candidates?.[0]?.groundingMetadata,
-            source: 'gemini'
-          }
-        } as Response
-      } as BlockCompleteChunk)
-      return
+      onChunk({ type: ChunkType.LLM_RESPONSE_CREATED })
+      return await processStream(response, 0).then(cleanup)
     }
 
-    // 等待接口返回流
     onChunk({ type: ChunkType.LLM_RESPONSE_CREATED })
     const userMessagesStream = await chat.sendMessageStream({
       message: messageContents as PartUnion,
@@ -394,105 +604,6 @@ export default class GeminiProvider extends BaseProvider {
       }
     })
 
-    const processToolUses = async (content: string, idx: number) => {
-      const toolResults = await parseAndCallTools(
-        content,
-        toolResponses,
-        onChunk,
-        idx,
-        mcpToolCallResponseToGeminiMessage,
-        mcpTools,
-        isVisionModel(model)
-      )
-      if (toolResults && toolResults.length > 0) {
-        history.push(messageContents)
-        const newChat = this.sdk.chats.create({
-          model: model.id,
-          config: generateContentConfig,
-          history: history as Content[]
-        })
-        const newStream = await newChat.sendMessageStream({
-          message: flatten(toolResults.map((ts) => (ts as Content).parts)) as PartUnion,
-          config: {
-            ...generateContentConfig,
-            abortSignal: abortController.signal
-          }
-        })
-        await processStream(newStream, idx + 1)
-      }
-    }
-
-    const processStream = async (stream: AsyncGenerator<GenerateContentResponse>, idx: number) => {
-      let content = ''
-      let final_time_completion_millsec = 0
-      let lastUsage: Usage | undefined = undefined
-      for await (const chunk of stream) {
-        if (window.keyv.get(EVENT_NAMES.CHAT_COMPLETION_PAUSED)) break
-
-        // --- Calculate Metrics ---
-        if (time_first_token_millsec == 0 && chunk.text !== undefined) {
-          // Update based on text arrival
-          time_first_token_millsec = new Date().getTime() - start_time_millsec
-        }
-
-        // 1. Text Content
-        if (chunk.text !== undefined) {
-          content += chunk.text
-          onChunk({ type: ChunkType.TEXT_DELTA, text: chunk.text })
-        }
-
-        // 2. Usage Data
-        if (chunk.usageMetadata) {
-          lastUsage = {
-            prompt_tokens: chunk.usageMetadata.promptTokenCount || 0,
-            completion_tokens: chunk.usageMetadata.candidatesTokenCount || 0,
-            total_tokens: chunk.usageMetadata.totalTokenCount || 0
-          }
-          final_time_completion_millsec = new Date().getTime() - start_time_millsec
-        }
-
-        // 4. Image Generation
-        const generateImage = this.processGeminiImageResponse(chunk, onChunk)
-        if (generateImage?.images?.length) {
-          onChunk({ type: ChunkType.IMAGE_COMPLETE, image: generateImage })
-        }
-
-        if (chunk.candidates?.[0]?.finishReason) {
-          if (chunk.text) {
-            onChunk({ type: ChunkType.TEXT_COMPLETE, text: content })
-          }
-          if (chunk.candidates?.[0]?.groundingMetadata) {
-            // 3. Grounding/Search Metadata
-            const groundingMetadata = chunk.candidates?.[0]?.groundingMetadata
-            onChunk({
-              type: ChunkType.LLM_WEB_SEARCH_COMPLETE,
-              llm_web_search: {
-                results: groundingMetadata,
-                source: WebSearchSource.GEMINI
-              }
-            } as LLMWebSearchCompleteChunk)
-          }
-          onChunk({
-            type: ChunkType.BLOCK_COMPLETE,
-            response: {
-              metrics: {
-                completion_tokens: lastUsage?.completion_tokens,
-                time_completion_millsec: final_time_completion_millsec,
-                time_first_token_millsec
-              },
-              usage: lastUsage
-            }
-          })
-        }
-        // --- End Incremental onChunk calls ---
-
-        // Call processToolUses AFTER potentially processing text content in this chunk
-        // This assumes tools might be specified within the text stream
-        // Note: parseAndCallTools inside should handle its own onChunk for tool responses
-        await processToolUses(content, idx)
-      }
-    }
-
     await processStream(userMessagesStream, 0).finally(cleanup)
 
     const final_time_completion_millsec = new Date().getTime() - start_time_millsec
@@ -841,4 +952,32 @@ export default class GeminiProvider extends BaseProvider {
   public generateImageByChat(): Promise<void> {
     throw new Error('Method not implemented.')
   }
+
+  public convertMcpTools<T>(mcpTools: MCPTool[]): T[] {
+    return mcpToolsToGeminiTools(mcpTools) as T[]
+  }
+
+  public mcpToolCallResponseToMessage = (mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model) => {
+    if ('toolUseId' in mcpToolResponse && mcpToolResponse.toolUseId) {
+      return mcpToolCallResponseToGeminiMessage(mcpToolResponse, resp, isVisionModel(model))
+    } else if ('toolCallId' in mcpToolResponse) {
+      const toolCallOut = {
+        role: 'user',
+        parts: [
+          {
+            functionResponse: {
+              id: mcpToolResponse.toolCallId,
+              name: mcpToolResponse.tool.id,
+              response: {
+                output: !resp.isError ? resp.content : undefined,
+                error: resp.isError ? resp.content : undefined
+              }
+            }
+          }
+        ]
+      } satisfies Content
+      return toolCallOut
+    }
+    return
+  }
 }
diff --git a/src/renderer/src/providers/AiProvider/OpenAICompatibleProvider.ts b/src/renderer/src/providers/AiProvider/OpenAICompatibleProvider.ts
index dce83cb6dc..444612eab0 100644
--- a/src/renderer/src/providers/AiProvider/OpenAICompatibleProvider.ts
+++ b/src/renderer/src/providers/AiProvider/OpenAICompatibleProvider.ts
@@ -31,10 +31,13 @@ import {
   Assistant,
   EFFORT_RATIO,
   FileTypes,
+  MCPCallToolResponse,
+  MCPTool,
   MCPToolResponse,
   Model,
   Provider,
   Suggestion,
+  ToolCallResponse,
   Usage,
   WebSearchSource
 } from '@renderer/types'
@@ -48,7 +51,12 @@ import {
   convertLinksToOpenRouter,
   convertLinksToZhipu
 } from '@renderer/utils/linkConverter'
-import { mcpToolCallResponseToOpenAICompatibleMessage, parseAndCallTools } from '@renderer/utils/mcp-tools'
+import {
+  mcpToolCallResponseToOpenAICompatibleMessage,
+  mcpToolsToOpenAIChatTools,
+  openAIToolsToMcpTool,
+  parseAndCallTools
+} from '@renderer/utils/mcp-tools'
 import { findFileBlocks, findImageBlocks, getMainTextContent } from '@renderer/utils/messageUtils/find'
 import { buildSystemPrompt } from '@renderer/utils/prompt'
 import { asyncGeneratorToReadableStream, readableStreamAsyncIterable } from '@renderer/utils/stream'
@@ -57,18 +65,22 @@ import OpenAI, { AzureOpenAI } from 'openai'
 import {
   ChatCompletionContentPart,
   ChatCompletionCreateParamsNonStreaming,
-  ChatCompletionMessageParam
+  ChatCompletionMessageParam,
+  ChatCompletionMessageToolCall,
+  ChatCompletionTool,
+  ChatCompletionToolMessageParam
 } from 'openai/resources'
 
 import { CompletionsParams } from '.'
-import OpenAIProvider from './OpenAIProvider'
+import { BaseOpenAiProvider } from './OpenAIProvider'
 
 // 1. 定义联合类型
 export type OpenAIStreamChunk =
   | { type: 'reasoning' | 'text-delta'; textDelta: string }
+  | { type: 'tool-calls'; delta: any }
   | { type: 'finish'; finishReason: any; usage: any; delta: any; chunk: any }
 
-export default class OpenAICompatibleProvider extends OpenAIProvider {
+export default class OpenAICompatibleProvider extends BaseOpenAiProvider {
   constructor(provider: Provider) {
     super(provider)
 
@@ -313,6 +325,24 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
     return {}
   }
 
+  public convertMcpTools<T>(mcpTools: MCPTool[]): T[] {
+    return mcpToolsToOpenAIChatTools(mcpTools) as T[]
+  }
+
+  public mcpToolCallResponseToMessage = (mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model) => {
+    if ('toolUseId' in mcpToolResponse && mcpToolResponse.toolUseId) {
+      return mcpToolCallResponseToOpenAICompatibleMessage(mcpToolResponse, resp, isVisionModel(model))
+    } else if ('toolCallId' in mcpToolResponse && mcpToolResponse.toolCallId) {
+      const toolCallOut: ChatCompletionToolMessageParam = {
+        role: 'tool',
+        tool_call_id: mcpToolResponse.toolCallId,
+        content: JSON.stringify(resp.content)
+      }
+      return toolCallOut
+    }
+    return
+  }
+
   /**
    * Generate completions for the assistant
    * @param messages - The messages
@@ -330,7 +360,7 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
     const defaultModel = getDefaultModel()
     const model = assistant.model || defaultModel
 
-    const { contextCount, maxTokens, streamOutput } = getAssistantSettings(assistant)
+    const { contextCount, maxTokens, streamOutput, enableToolUse } = getAssistantSettings(assistant)
     const isEnabledWebSearch = assistant.enableWebSearch || !!assistant.webSearchProviderId
     messages = addImageFileToContents(messages)
     const enableReasoning =
@@ -344,7 +374,9 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
         content: `Formatting re-enabled${systemMessage ? '\n' + systemMessage.content : ''}`
       }
     }
-    if (mcpTools && mcpTools.length > 0) {
+    const { tools } = this.setupToolsConfig<ChatCompletionTool>({ mcpTools, model, enableToolUse })
+
+    if (this.useSystemPromptForTools) {
       systemMessage.content = buildSystemPrompt(systemMessage.content || '', mcpTools)
     }
 
@@ -379,53 +411,86 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
 
     const toolResponses: MCPToolResponse[] = []
 
-    const processToolUses = async (content: string, idx: number) => {
-      const toolResults = await parseAndCallTools(
+    const processToolResults = async (toolResults: Awaited<ReturnType<typeof parseAndCallTools>>, idx: number) => {
+      if (toolResults.length === 0) return
+
+      toolResults.forEach((ts) => reqMessages.push(ts as ChatCompletionMessageParam))
+
+      console.debug('[tool] reqMessages before processing', model.id, reqMessages)
+      reqMessages = processReqMessages(model, reqMessages)
+      console.debug('[tool] reqMessages', model.id, reqMessages)
+
+      onChunk({ type: ChunkType.LLM_RESPONSE_CREATED })
+      const newStream = await this.sdk.chat.completions
+        // @ts-ignore key is not typed
+        .create(
+          {
+            model: model.id,
+            messages: reqMessages,
+            temperature: this.getTemperature(assistant, model),
+            top_p: this.getTopP(assistant, model),
+            max_tokens: maxTokens,
+            keep_alive: this.keepAliveTime,
+            stream: isSupportStreamOutput(),
+            tools: !isEmpty(tools) ? tools : undefined,
+            ...getOpenAIWebSearchParams(assistant, model),
+            ...this.getReasoningEffort(assistant, model),
+            ...this.getProviderSpecificParameters(assistant, model),
+            ...this.getCustomParameters(assistant)
+          },
+          {
+            signal
+          }
+        )
+      await processStream(newStream, idx + 1)
+    }
+
+    const processToolCalls = async (mcpTools, toolCalls: ChatCompletionMessageToolCall[]) => {
+      const mcpToolResponses = toolCalls
+        .map((toolCall) => {
+          const mcpTool = openAIToolsToMcpTool(mcpTools, toolCall as ChatCompletionMessageToolCall)
+          if (!mcpTool) return undefined
+
+          const parsedArgs = (() => {
+            try {
+              return JSON.parse(toolCall.function.arguments)
+            } catch {
+              return toolCall.function.arguments
+            }
+          })()
+
+          return {
+            id: toolCall.id,
+            toolCallId: toolCall.id,
+            tool: mcpTool,
+            arguments: parsedArgs,
+            status: 'pending'
+          } as ToolCallResponse
+        })
+        .filter((t): t is ToolCallResponse => typeof t !== 'undefined')
+      return await parseAndCallTools(
+        mcpToolResponses,
+        toolResponses,
+        onChunk,
+        this.mcpToolCallResponseToMessage,
+        model,
+        mcpTools
+      )
+    }
+
+    const processToolUses = async (content: string) => {
+      return await parseAndCallTools(
         content,
         toolResponses,
         onChunk,
-        idx,
-        mcpToolCallResponseToOpenAICompatibleMessage,
-        mcpTools,
-        isVisionModel(model)
+        this.mcpToolCallResponseToMessage,
+        model,
+        mcpTools
       )
-
-      if (toolResults.length > 0) {
-        reqMessages.push({
-          role: 'assistant',
-          content: content
-        } as ChatCompletionMessageParam)
-        toolResults.forEach((ts) => reqMessages.push(ts as ChatCompletionMessageParam))
-
-        reqMessages = processReqMessages(model, reqMessages)
-        const newStream = await this.sdk.chat.completions
-          // @ts-ignore key is not typed
-          .create(
-            {
-              model: model.id,
-              messages: reqMessages,
-              temperature: this.getTemperature(assistant, model),
-              top_p: this.getTopP(assistant, model),
-              max_tokens: maxTokens,
-              keep_alive: this.keepAliveTime,
-              stream: isSupportStreamOutput(),
-              // tools: tools,
-              service_tier: this.getServiceTier(model),
-              ...getOpenAIWebSearchParams(assistant, model),
-              ...this.getReasoningEffort(assistant, model),
-              ...this.getProviderSpecificParameters(assistant, model),
-              ...this.getCustomParameters(assistant)
-            },
-            {
-              signal,
-              timeout: this.getTimeout(model)
-            }
-          )
-        await processStream(newStream, idx + 1)
-      }
     }
 
     const processStream = async (stream: any, idx: number) => {
+      const toolCalls: ChatCompletionMessageToolCall[] = []
       // Handle non-streaming case (already returns early, no change needed here)
       if (!isSupportStreamOutput()) {
         const time_completion_millsec = new Date().getTime() - start_time_millsec
@@ -439,10 +504,59 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
         // Create a synthetic usage object if stream.usage is undefined
         const finalUsage = stream.usage
         // Separate onChunk calls for text and usage/metrics
-        if (stream.choices[0].message?.content) {
-          onChunk({ type: ChunkType.TEXT_COMPLETE, text: stream.choices[0].message.content })
+        let content = ''
+        stream.choices.forEach((choice) => {
+          // reasoning
+          if (choice.message.reasoning) {
+            onChunk({ type: ChunkType.THINKING_DELTA, text: choice.message.reasoning })
+            onChunk({
+              type: ChunkType.THINKING_COMPLETE,
+              text: choice.message.reasoning,
+              thinking_millsec: time_completion_millsec
+            })
+          }
+          // text
+          if (choice.message.content) {
+            content += choice.message.content
+            onChunk({ type: ChunkType.TEXT_DELTA, text: choice.message.content })
+          }
+          // tool call
+          if (choice.message.tool_calls && choice.message.tool_calls.length) {
+            choice.message.tool_calls.forEach((t) => toolCalls.push(t))
+          }
+
+          reqMessages.push({
+            role: choice.message.role,
+            content: choice.message.content,
+            tool_calls: toolCalls.length
+              ? toolCalls.map((toolCall) => ({
+                  id: toolCall.id,
+                  function: {
+                    ...toolCall.function,
+                    arguments:
+                      typeof toolCall.function.arguments === 'string'
+                        ? toolCall.function.arguments
+                        : JSON.stringify(toolCall.function.arguments)
+                  },
+                  type: 'function'
+                }))
+              : undefined
+          })
+        })
+
+        if (content.length) {
+          onChunk({ type: ChunkType.TEXT_COMPLETE, text: content })
         }
 
+        const toolResults: Awaited<ReturnType<typeof parseAndCallTools>> = []
+        if (toolCalls.length) {
+          toolResults.push(...(await processToolCalls(mcpTools, toolCalls)))
+        }
+        if (stream.choices[0].message?.content) {
+          toolResults.push(...(await processToolUses(stream.choices[0].message?.content)))
+        }
+        await processToolResults(toolResults, idx)
+
         // Always send usage and metrics data
         onChunk({ type: ChunkType.BLOCK_COMPLETE, response: { usage: finalUsage, metrics: finalMetrics } })
         return
@@ -486,6 +600,9 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
           if (delta?.content) {
             yield { type: 'text-delta', textDelta: delta.content }
           }
+          if (delta?.tool_calls) {
+            yield { type: 'tool-calls', delta: delta }
+          }
 
           const finishReason = chunk.choices[0]?.finish_reason
           if (!isEmpty(finishReason)) {
@@ -563,6 +680,25 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
             onChunk({ type: ChunkType.TEXT_DELTA, text: textDelta })
             break
           }
+          case 'tool-calls': {
+            chunk.delta.tool_calls.forEach((toolCall) => {
+              const { id, index, type, function: fun } = toolCall
+              if (id && type === 'function' && fun) {
+                const { name, arguments: args } = fun
+                toolCalls.push({
+                  id,
+                  function: {
+                    name: name || '',
+                    arguments: args || ''
+                  },
+                  type: 'function'
+                })
+              } else if (fun?.arguments) {
+                toolCalls[index].function.arguments += fun.arguments
+              }
+            })
+            break
+          }
           case 'finish': {
             const finishReason = chunk.finishReason
             const usage = chunk.usage
@@ -624,7 +760,33 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
                 } as LLMWebSearchCompleteChunk)
               }
             }
-            await processToolUses(content, idx)
+            reqMessages.push({
+              role: 'assistant',
+              content: content,
+              tool_calls: toolCalls.length
+                ? toolCalls.map((toolCall) => ({
+                    id: toolCall.id,
+                    function: {
+                      ...toolCall.function,
+                      arguments:
+                        typeof toolCall.function.arguments === 'string'
+                          ? toolCall.function.arguments
+                          : JSON.stringify(toolCall.function.arguments)
+                    },
+                    type: 'function'
+                  }))
+                : undefined
+            })
+            let toolResults: Awaited<ReturnType<typeof parseAndCallTools>> = []
+            if (toolCalls.length) {
+              toolResults = await processToolCalls(mcpTools, toolCalls)
+            }
+            if (content.length) {
+              toolResults = toolResults.concat(await processToolUses(content))
+            }
+            if (toolResults.length) {
+              await processToolResults(toolResults, idx)
+            }
             onChunk({
               type: ChunkType.BLOCK_COMPLETE,
               response: {
@@ -657,7 +819,7 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
           max_tokens: maxTokens,
           keep_alive: this.keepAliveTime,
           stream: isSupportStreamOutput(),
-          // tools: tools,
+          tools: !isEmpty(tools) ? tools : undefined,
           service_tier: this.getServiceTier(model),
           ...getOpenAIWebSearchParams(assistant, model),
           ...this.getReasoningEffort(assistant, model),
diff --git a/src/renderer/src/providers/AiProvider/OpenAIProvider.ts b/src/renderer/src/providers/AiProvider/OpenAIProvider.ts
index b1fc7a6c09..32341d020d 100644
--- a/src/renderer/src/providers/AiProvider/OpenAIProvider.ts
+++ b/src/renderer/src/providers/AiProvider/OpenAIProvider.ts
@@ -21,10 +21,13 @@ import {
   Assistant,
   FileTypes,
   GenerateImageParams,
+  MCPCallToolResponse,
+  MCPTool,
   MCPToolResponse,
   Model,
   Provider,
   Suggestion,
+  ToolCallResponse,
   Usage,
   WebSearchSource
 } from '@renderer/types'
@@ -33,7 +36,12 @@ import { Message } from '@renderer/types/newMessage'
 import { removeSpecialCharactersForTopicName } from '@renderer/utils'
 import { addImageFileToContents } from '@renderer/utils/formats'
 import { convertLinks } from '@renderer/utils/linkConverter'
-import { mcpToolCallResponseToOpenAIMessage, parseAndCallTools } from '@renderer/utils/mcp-tools'
+import {
+  mcpToolCallResponseToOpenAIMessage,
+  mcpToolsToOpenAIResponseTools,
+  openAIToolsToMcpTool,
+  parseAndCallTools
+} from '@renderer/utils/mcp-tools'
 import { findFileBlocks, findImageBlocks, getMainTextContent } from '@renderer/utils/messageUtils/find'
 import { buildSystemPrompt } from '@renderer/utils/prompt'
 import { isEmpty, takeRight } from 'lodash'
@@ -45,7 +53,7 @@ import { FileLike, toFile } from 'openai/uploads'
 import { CompletionsParams } from '.'
 import BaseProvider from './BaseProvider'
 
-export default class OpenAIProvider extends BaseProvider {
+export abstract class BaseOpenAiProvider extends BaseProvider {
   protected sdk: OpenAI
 
   constructor(provider: Provider) {
@@ -61,6 +69,14 @@ export default class OpenAIProvider extends BaseProvider {
     })
   }
 
+  abstract convertMcpTools<T>(mcpTools: MCPTool[]): T[]
+
+  abstract mcpToolCallResponseToMessage: (
+    mcpToolResponse: MCPToolResponse,
+    resp: MCPCallToolResponse,
+    model: Model
+  ) => OpenAI.Responses.ResponseInputItem | ChatCompletionMessageParam | undefined
+
   /**
    * Extract the file content from the message
    * @param message - The message
@@ -91,16 +107,23 @@ export default class OpenAIProvider extends BaseProvider {
     return ''
   }
 
-  private async getReponseMessageParam(message: Message, model: Model): Promise<OpenAI.Responses.EasyInputMessage> {
+  private async getReponseMessageParam(message: Message, model: Model): Promise<OpenAI.Responses.ResponseInputItem> {
     const isVision = isVisionModel(model)
     const content = await this.getMessageContent(message)
     const fileBlocks = findFileBlocks(message)
     const imageBlocks = findImageBlocks(message)
 
     if (fileBlocks.length === 0 && imageBlocks.length === 0) {
-      return {
-        role: message.role === 'system' ? 'user' : message.role,
-        content: content ? [{ type: 'input_text', text: content }] : []
+      if (message.role === 'assistant') {
+        return {
+          role: 'assistant',
+          content: content
+        }
+      } else {
+        return {
+          role: message.role === 'system' ? 'user' : message.role,
+          content: content ? [{ type: 'input_text', text: content }] : []
+        } as OpenAI.Responses.EasyInputMessage
       }
     }
 
@@ -285,10 +308,8 @@ export default class OpenAIProvider extends BaseProvider {
     }
     const defaultModel = getDefaultModel()
     const model = assistant.model || defaultModel
-    const { contextCount, maxTokens, streamOutput } = getAssistantSettings(assistant)
-
+    const { contextCount, maxTokens, streamOutput, enableToolUse } = getAssistantSettings(assistant)
     const isEnabledWebSearch = assistant.enableWebSearch || !!assistant.webSearchProviderId
-    onChunk({ type: ChunkType.LLM_RESPONSE_CREATED })
     // 退回到 OpenAI 兼容模式
     if (isOpenAIWebSearch(model)) {
       const systemMessage = { role: 'system', content: assistant.prompt || '' }
@@ -387,7 +408,7 @@ export default class OpenAIProvider extends BaseProvider {
       })
       return
     }
-    const tools: OpenAI.Responses.Tool[] = []
+    let tools: OpenAI.Responses.Tool[] = []
     if (isEnabledWebSearch) {
       tools.push({
         type: 'web_search_preview'
@@ -408,7 +429,15 @@ export default class OpenAIProvider extends BaseProvider {
       systemMessage.role = 'developer'
     }
 
-    if (mcpTools && mcpTools.length > 0) {
+    const { tools: extraTools } = this.setupToolsConfig<OpenAI.Responses.Tool>({
+      mcpTools,
+      model,
+      enableToolUse
+    })
+
+    tools = tools.concat(extraTools)
+
+    if (this.useSystemPromptForTools) {
       systemMessageInput.text = buildSystemPrompt(systemMessageInput.text || '', mcpTools)
     }
     systemMessageContent.push(systemMessageInput)
@@ -418,7 +447,7 @@ export default class OpenAIProvider extends BaseProvider {
     )
 
     onFilterMessages(_messages)
-    const userMessage: OpenAI.Responses.EasyInputMessage[] = []
+    const userMessage: OpenAI.Responses.ResponseInputItem[] = []
     for (const message of _messages) {
       userMessage.push(await this.getReponseMessageParam(message, model))
     }
@@ -431,7 +460,7 @@ export default class OpenAIProvider extends BaseProvider {
     const { signal } = abortController
 
     // 当 systemMessage 内容为空时不发送 systemMessage
-    let reqMessages: OpenAI.Responses.EasyInputMessage[]
+    let reqMessages: OpenAI.Responses.ResponseInput
     if (!systemMessage.content) {
       reqMessages = [...userMessage]
     } else {
@@ -440,48 +469,84 @@ export default class OpenAIProvider extends BaseProvider {
 
     const toolResponses: MCPToolResponse[] = []
 
-    const processToolUses = async (content: string, idx: number) => {
-      const toolResults = await parseAndCallTools(
+    const processToolResults = async (toolResults: Awaited<ReturnType<typeof parseAndCallTools>>, idx: number) => {
+      if (toolResults.length === 0) return
+
+      toolResults.forEach((ts) => reqMessages.push(ts as OpenAI.Responses.EasyInputMessage))
+
+      onChunk({ type: ChunkType.LLM_RESPONSE_CREATED })
+      const stream = await this.sdk.responses.create(
+        {
+          model: model.id,
+          input: reqMessages,
+          temperature: this.getTemperature(assistant, model),
+          top_p: this.getTopP(assistant, model),
+          max_output_tokens: maxTokens,
+          stream: streamOutput,
+          tools: !isEmpty(tools) ? tools : undefined,
+          service_tier: this.getServiceTier(model),
+          ...this.getResponseReasoningEffort(assistant, model),
+          ...this.getCustomParameters(assistant)
+        },
+        {
+          signal,
+          timeout: this.getTimeout(model)
+        }
+      )
+      await processStream(stream, idx + 1)
+    }
+
+    const processToolCalls = async (mcpTools, toolCalls: OpenAI.Responses.ResponseFunctionToolCall[]) => {
+      const mcpToolResponses = toolCalls
+        .map((toolCall) => {
+          const mcpTool = openAIToolsToMcpTool(mcpTools, toolCall as OpenAI.Responses.ResponseFunctionToolCall)
+          if (!mcpTool) return undefined
+
+          const parsedArgs = (() => {
+            try {
+              return JSON.parse(toolCall.arguments)
+            } catch {
+              return toolCall.arguments
+            }
+          })()
+
+          return {
+            id: toolCall.call_id,
+            toolCallId: toolCall.call_id,
+            tool: mcpTool,
+            arguments: parsedArgs,
+            status: 'pending'
+          } as ToolCallResponse
+        })
+        .filter((t): t is ToolCallResponse => typeof t !== 'undefined')
+
+      return await parseAndCallTools<OpenAI.Responses.ResponseInputItem | ChatCompletionMessageParam>(
+        mcpToolResponses,
+        toolResponses,
+        onChunk,
+        this.mcpToolCallResponseToMessage,
+        model,
+        mcpTools
+      )
+    }
+
+    const processToolUses = async (content: string) => {
+      return await parseAndCallTools(
         content,
         toolResponses,
         onChunk,
-        idx,
-        mcpToolCallResponseToOpenAIMessage,
-        mcpTools,
-        isVisionModel(model)
+        this.mcpToolCallResponseToMessage,
+        model,
+        mcpTools
       )
-
-      if (toolResults.length > 0) {
-        reqMessages.push({
-          role: 'assistant',
-          content: content
-        })
-        toolResults.forEach((ts) => reqMessages.push(ts as OpenAI.Responses.EasyInputMessage))
-        const newStream = await this.sdk.responses.create(
-          {
-            model: model.id,
-            input: reqMessages,
-            temperature: this.getTemperature(assistant, model),
-            top_p: this.getTopP(assistant, model),
-            max_output_tokens: maxTokens,
-            stream: true,
-            service_tier: this.getServiceTier(model),
-            ...this.getResponseReasoningEffort(assistant, model),
-            ...this.getCustomParameters(assistant)
-          },
-          {
-            signal,
-            timeout: this.getTimeout(model)
-          }
-        )
-        await processStream(newStream, idx + 1)
-      }
     }
 
     const processStream = async (
       stream: Stream<OpenAI.Responses.ResponseStreamEvent> | OpenAI.Responses.Response,
       idx: number
     ) => {
+      const toolCalls: OpenAI.Responses.ResponseFunctionToolCall[] = []
+
       if (!streamOutput) {
         const nonStream = stream as OpenAI.Responses.Response
         const time_completion_millsec = new Date().getTime() - start_time_millsec
@@ -499,11 +564,15 @@ export default class OpenAIProvider extends BaseProvider {
           prompt_tokens: nonStream.usage?.input_tokens || 0,
           total_tokens
         }
+        let content = ''
+
         for (const output of nonStream.output) {
           switch (output.type) {
             case 'message':
               if (output.content[0].type === 'output_text') {
+                onChunk({ type: ChunkType.TEXT_DELTA, text: output.content[0].text })
                 onChunk({ type: ChunkType.TEXT_COMPLETE, text: output.content[0].text })
+                content += output.content[0].text
                 if (output.content[0].annotations && output.content[0].annotations.length > 0) {
                   onChunk({
                     type: ChunkType.LLM_WEB_SEARCH_COMPLETE,
@@ -522,8 +591,32 @@ export default class OpenAIProvider extends BaseProvider {
                 thinking_millsec: new Date().getTime() - start_time_millsec
               })
               break
+            case 'function_call':
+              toolCalls.push(output)
           }
         }
+
+        if (content) {
+          reqMessages.push({
+            role: 'assistant',
+            content: content
+          })
+        }
+        if (toolCalls.length) {
+          toolCalls.forEach((toolCall) => {
+            reqMessages.push(toolCall)
+          })
+        }
+
+        const toolResults: Awaited<ReturnType<typeof parseAndCallTools>> = []
+        if (toolCalls.length) {
+          toolResults.push(...(await processToolCalls(mcpTools, toolCalls)))
+        }
+        if (content.length) {
+          toolResults.push(...(await processToolUses(content)))
+        }
+        await processToolResults(toolResults, idx)
+
         onChunk({
           type: ChunkType.BLOCK_COMPLETE,
           response: {
@@ -534,6 +627,9 @@ export default class OpenAIProvider extends BaseProvider {
         return
       }
       let content = ''
+
+      const outputItems: OpenAI.Responses.ResponseOutputItem[] = []
+
       let lastUsage: Usage | undefined = undefined
       let final_time_completion_millsec_delta = 0
       for await (const chunk of stream as Stream<OpenAI.Responses.ResponseStreamEvent>) {
@@ -544,6 +640,12 @@ export default class OpenAIProvider extends BaseProvider {
           case 'response.created':
             time_first_token_millsec = new Date().getTime()
             break
+          case 'response.output_item.added':
+            if (chunk.item.type === 'function_call') {
+              outputItems.push(chunk.item)
+            }
+            break
+
           case 'response.reasoning_summary_text.delta':
             onChunk({
               type: ChunkType.THINKING_DELTA,
@@ -571,6 +673,21 @@ export default class OpenAIProvider extends BaseProvider {
               text: chunk.text
             })
             break
+          case 'response.function_call_arguments.done': {
+            const outputItem: OpenAI.Responses.ResponseOutputItem | undefined = outputItems.find(
+              (item) => item.id === chunk.item_id
+            )
+            if (outputItem) {
+              if (outputItem.type === 'function_call') {
+                toolCalls.push({
+                  ...outputItem,
+                  arguments: chunk.arguments
+                })
+              }
+            }
+
+            break
+          }
           case 'response.content_part.done':
             if (chunk.part.type === 'output_text' && chunk.part.annotations && chunk.part.annotations.length > 0) {
               onChunk({
@@ -607,9 +724,31 @@ export default class OpenAIProvider extends BaseProvider {
             })
             break
         }
+
+        // --- End of Incremental onChunk calls ---
+      } // End of for await loop
+      if (content) {
+        reqMessages.push({
+          role: 'assistant',
+          content: content
+        })
+      }
+      if (toolCalls.length) {
+        toolCalls.forEach((toolCall) => {
+          reqMessages.push(toolCall)
+        })
       }
 
-      await processToolUses(content, idx)
+      // Call processToolUses AFTER the loop finishes processing the main stream content
+      // Note: parseAndCallTools inside processToolUses should handle its own onChunk for tool responses
+      const toolResults: Awaited<ReturnType<typeof parseAndCallTools>> = []
+      if (toolCalls.length) {
+        toolResults.push(...(await processToolCalls(mcpTools, toolCalls)))
+      }
+      if (content) {
+        toolResults.push(...(await processToolUses(content)))
+      }
+      await processToolResults(toolResults, idx)
 
       onChunk({
         type: ChunkType.BLOCK_COMPLETE,
@@ -624,6 +763,7 @@ export default class OpenAIProvider extends BaseProvider {
       })
     }
 
+    onChunk({ type: ChunkType.LLM_RESPONSE_CREATED })
     const stream = await this.sdk.responses.create(
       {
         model: model.id,
@@ -1072,3 +1212,31 @@ export default class OpenAIProvider extends BaseProvider {
     return data.data[0].embedding.length
   }
 }
+
+export default class OpenAIProvider extends BaseOpenAiProvider {
+  constructor(provider: Provider) {
+    super(provider)
+  }
+
+  public convertMcpTools<T>(mcpTools: MCPTool[]) {
+    return mcpToolsToOpenAIResponseTools(mcpTools) as T[]
+  }
+
+  public mcpToolCallResponseToMessage = (
+    mcpToolResponse: MCPToolResponse,
+    resp: MCPCallToolResponse,
+    model: Model
+  ): OpenAI.Responses.ResponseInputItem | undefined => {
+    if ('toolUseId' in mcpToolResponse && mcpToolResponse.toolUseId) {
+      return mcpToolCallResponseToOpenAIMessage(mcpToolResponse, resp, isVisionModel(model))
+    } else if ('toolCallId' in mcpToolResponse && mcpToolResponse.toolCallId) {
+      const toolCallOut: OpenAI.Responses.ResponseInputItem = {
+        type: 'function_call_output',
+        call_id: mcpToolResponse.toolCallId,
+        output: JSON.stringify(resp.content)
+      }
+      return toolCallOut
+    }
+    return
+  }
+}
diff --git a/src/renderer/src/services/AssistantService.ts b/src/renderer/src/services/AssistantService.ts
index 4ad50c29ef..1bef2ec2b7 100644
--- a/src/renderer/src/services/AssistantService.ts
+++ b/src/renderer/src/services/AssistantService.ts
@@ -107,6 +107,7 @@ export const getAssistantSettings = (assistant: Assistant): AssistantSettings =>
     enableMaxTokens: assistant?.settings?.enableMaxTokens ?? false,
     maxTokens: getAssistantMaxTokens(),
     streamOutput: assistant?.settings?.streamOutput ?? true,
+    enableToolUse: assistant?.settings?.enableToolUse ?? false,
     hideMessages: assistant?.settings?.hideMessages ?? false,
     defaultModel: assistant?.defaultModel ?? undefined,
     customParameters: assistant?.settings?.customParameters ?? []
diff --git a/src/renderer/src/store/thunk/messageThunk.ts b/src/renderer/src/store/thunk/messageThunk.ts
index ac285298b7..eadca50d73 100644
--- a/src/renderer/src/store/thunk/messageThunk.ts
+++ b/src/renderer/src/store/thunk/messageThunk.ts
@@ -423,7 +423,17 @@ const fetchAndProcessAssistantResponseImpl = async (
         }
       },
       onToolCallInProgress: (toolResponse: MCPToolResponse) => {
-        if (toolResponse.status === 'invoking') {
+        if (lastBlockType === MessageBlockType.UNKNOWN && lastBlockId) {
+          lastBlockType = MessageBlockType.TOOL
+          const changes = {
+            type: MessageBlockType.TOOL,
+            status: MessageBlockStatus.PROCESSING,
+            metadata: { rawMcpToolResponse: toolResponse }
+          }
+          dispatch(updateOneBlock({ id: lastBlockId, changes }))
+          saveUpdatedBlockToDB(lastBlockId, assistantMsgId, topicId, getState)
+          toolCallIdToBlockIdMap.set(toolResponse.id, lastBlockId)
+        } else if (toolResponse.status === 'invoking') {
           const toolBlock = createToolBlock(assistantMsgId, toolResponse.id, {
             toolName: toolResponse.tool.name,
             status: MessageBlockStatus.PROCESSING,
diff --git a/src/renderer/src/types/index.ts b/src/renderer/src/types/index.ts
index 3f05e727b9..95b3756980 100644
--- a/src/renderer/src/types/index.ts
+++ b/src/renderer/src/types/index.ts
@@ -55,6 +55,7 @@ export type AssistantSettings = {
   maxTokens: number | undefined
   enableMaxTokens: boolean
   streamOutput: boolean
+  enableToolUse: boolean
   hideMessages: boolean
   defaultModel?: Model
   customParameters?: AssistantSettingCustomParameters[]
@@ -570,13 +571,25 @@ export interface MCPConfig {
   servers: MCPServer[]
 }
 
-export interface MCPToolResponse {
-  id: string // tool call id, it should be unique
-  tool: MCPTool // tool info
+interface BaseToolResponse {
+  id: string // unique id
+  tool: MCPTool
+  arguments: Record<string, unknown> | undefined
   status: string // 'invoking' | 'done'
   response?: any
 }
 
+export interface ToolUseResponse extends BaseToolResponse {
+  toolUseId: string
+}
+
+export interface ToolCallResponse extends BaseToolResponse {
+  // gemini tool call id might be undefined
+  toolCallId?: string
+}
+
+export type MCPToolResponse = ToolUseResponse | ToolCallResponse
+
 export interface MCPToolResultContent {
   type: 'text' | 'image' | 'audio' | 'resource'
   text?: string
@@ -586,6 +599,7 @@ export interface MCPToolResultContent {
     uri?: string
     text?: string
     mimeType?: string
+    blob?: string
   }
 }
 
diff --git a/src/renderer/src/utils/mcp-tools.ts b/src/renderer/src/utils/mcp-tools.ts
index e8abc2f4bc..b484d0cc18 100644
--- a/src/renderer/src/utils/mcp-tools.ts
+++ b/src/renderer/src/utils/mcp-tools.ts
@@ -1,18 +1,31 @@
-import { ContentBlockParam, ToolUnion, ToolUseBlock } from '@anthropic-ai/sdk/resources'
-import { MessageParam } from '@anthropic-ai/sdk/resources'
-import { Content, FunctionCall, Part } from '@google/genai'
+import {
+  ContentBlockParam,
+  MessageParam,
+  ToolResultBlockParam,
+  ToolUnion,
+  ToolUseBlock
+} from '@anthropic-ai/sdk/resources'
+import { Content, FunctionCall, Part, Tool, Type as GeminiSchemaType } from '@google/genai'
+import { isVisionModel } from '@renderer/config/models'
 import store from '@renderer/store'
 import { addMCPServer } from '@renderer/store/mcp'
-import { MCPCallToolResponse, MCPServer, MCPTool, MCPToolResponse } from '@renderer/types'
+import { MCPCallToolResponse, MCPServer, MCPTool, MCPToolResponse, Model, ToolUseResponse } from '@renderer/types'
 import type { MCPToolCompleteChunk, MCPToolInProgressChunk } from '@renderer/types/chunk'
 import { ChunkType } from '@renderer/types/chunk'
+import { isArray, isObject, pull, transform } from 'lodash'
 import { nanoid } from 'nanoid'
 import OpenAI from 'openai'
-import { ChatCompletionContentPart, ChatCompletionMessageParam, ChatCompletionMessageToolCall } from 'openai/resources'
+import {
+  ChatCompletionContentPart,
+  ChatCompletionMessageParam,
+  ChatCompletionMessageToolCall,
+  ChatCompletionTool
+} from 'openai/resources'
 
 import { CompletionsParams } from '../providers/AiProvider'
 
 const MCP_AUTO_INSTALL_SERVER_NAME = '@cherry/mcp-auto-install'
+const EXTRA_SCHEMA_KEYS = ['schema', 'headers']
 
 // const ensureValidSchema = (obj: Record<string, any>) => {
 //   // Filter out unsupported keys for Gemini
@@ -153,77 +166,116 @@ const MCP_AUTO_INSTALL_SERVER_NAME = '@cherry/mcp-auto-install'
 //   return processedProperties
 // }
 
-// export function mcpToolsToOpenAITools(mcpTools: MCPTool[]): Array<ChatCompletionTool> {
-//   return mcpTools.map((tool) => ({
-//     type: 'function',
-//     name: tool.name,
-//     function: {
-//       name: tool.id,
-//       description: tool.description,
-//       parameters: {
-//         type: 'object',
-//         properties: filterPropertieAttributes(tool)
-//       }
-//     }
-//   }))
-// }
-
-export function openAIToolsToMcpTool(
-  mcpTools: MCPTool[] | undefined,
-  llmTool: ChatCompletionMessageToolCall
-): MCPTool | undefined {
-  if (!mcpTools) {
-    return undefined
+export function filterProperties(
+  properties: Record<string, any> | string | number | boolean | Array<Record<string, any> | string | number | boolean>,
+  supportedKeys: string[]
+) {
+  // If it is an array, recursively process each element
+  if (isArray(properties)) {
+    return properties.map((item) => filterProperties(item, supportedKeys))
   }
 
-  const tool = mcpTools.find(
-    (mcptool) => mcptool.id === llmTool.function.name || mcptool.name === llmTool.function.name
-  )
+  // If it is an object, recursively process each property
+  if (isObject(properties)) {
+    return transform(
+      properties,
+      (result, value, key) => {
+        if (key === 'properties') {
+          result[key] = transform(value, (acc, v, k) => {
+            acc[k] = filterProperties(v, supportedKeys)
+          })
 
-  if (!tool) {
-    console.warn('No MCP Tool found for tool call:', llmTool)
-    return undefined
+          result['additionalProperties'] = false
+          result['required'] = pull(Object.keys(value), ...EXTRA_SCHEMA_KEYS)
+        } else if (key === 'oneOf') {
+          // openai only supports anyOf
+          result['anyOf'] = filterProperties(value, supportedKeys)
+        } else if (supportedKeys.includes(key)) {
+          result[key] = filterProperties(value, supportedKeys)
+          if (key === 'type' && value === 'object') {
+            result['additionalProperties'] = false
+          }
+        }
+      },
+      {}
+    )
   }
 
-  console.log(
-    `[MCP] OpenAI Tool to MCP Tool: ${tool.serverName} ${tool.name}`,
-    tool,
-    'args',
-    llmTool.function.arguments
-  )
-  // use this to parse the arguments and avoid parsing errors
-  let args: any = {}
-  try {
-    args = JSON.parse(llmTool.function.arguments)
-  } catch (e) {
-    console.error('Error parsing arguments', e)
-  }
-
-  return {
-    id: tool.id,
-    serverId: tool.serverId,
-    serverName: tool.serverName,
-    name: tool.name,
-    description: tool.description,
-    inputSchema: args
-  }
+  // Return other types directly (e.g., string, number, etc.)
+  return properties
 }
 
-export async function callMCPTool(tool: MCPTool): Promise<MCPCallToolResponse> {
-  console.log(`[MCP] Calling Tool: ${tool.serverName} ${tool.name}`, tool)
+export function mcpToolsToOpenAIResponseTools(mcpTools: MCPTool[]): OpenAI.Responses.Tool[] {
+  const schemaKeys = ['type', 'description', 'items', 'enum', 'additionalProperties', 'anyof']
+  return mcpTools.map(
+    (tool) =>
+      ({
+        type: 'function',
+        name: tool.id,
+        parameters: {
+          type: 'object',
+          properties: filterProperties(tool.inputSchema, schemaKeys).properties,
+          required: pull(Object.keys(tool.inputSchema.properties), ...EXTRA_SCHEMA_KEYS),
+          additionalProperties: false
+        },
+        strict: true
+      }) satisfies OpenAI.Responses.Tool
+  )
+}
+
+export function mcpToolsToOpenAIChatTools(mcpTools: MCPTool[]): Array<ChatCompletionTool> {
+  return mcpTools.map(
+    (tool) =>
+      ({
+        type: 'function',
+        function: {
+          name: tool.id,
+          description: tool.description,
+          parameters: {
+            type: 'object',
+            properties: tool.inputSchema.properties,
+            required: tool.inputSchema.required
+          }
+        }
+      }) as ChatCompletionTool
+  )
+}
+
+export function openAIToolsToMcpTool(
+  mcpTools: MCPTool[],
+  toolCall: OpenAI.Responses.ResponseFunctionToolCall | ChatCompletionMessageToolCall
+): MCPTool | undefined {
+  const tool = mcpTools.find((mcpTool) => {
+    if ('name' in toolCall) {
+      return mcpTool.id === toolCall.name || mcpTool.name === toolCall.name
+    } else {
+      return mcpTool.id === toolCall.function.name || mcpTool.name === toolCall.function.name
+    }
+  })
+
+  if (!tool) {
+    console.warn('No MCP Tool found for tool call:', toolCall)
+    return undefined
+  }
+
+  return tool
+}
+
+export async function callMCPTool(toolResponse: MCPToolResponse): Promise<MCPCallToolResponse> {
+  console.log(`[MCP] Calling Tool: ${toolResponse.tool.serverName} ${toolResponse.tool.name}`, toolResponse.tool)
   try {
-    const server = getMcpServerByTool(tool)
+    const server = getMcpServerByTool(toolResponse.tool)
 
     if (!server) {
-      throw new Error(`Server not found: ${tool.serverName}`)
+      throw new Error(`Server not found: ${toolResponse.tool.serverName}`)
     }
 
     const resp = await window.api.mcp.callTool({
       server,
-      name: tool.name,
-      args: tool.inputSchema
+      name: toolResponse.tool.name,
+      args: toolResponse.arguments
     })
-    if (tool.serverName === MCP_AUTO_INSTALL_SERVER_NAME) {
+    if (toolResponse.tool.serverName === MCP_AUTO_INSTALL_SERVER_NAME) {
       if (resp.data) {
         const mcpServer: MCPServer = {
           id: `f${nanoid()}`,
@@ -241,16 +293,16 @@ export async function callMCPTool(tool: MCPTool): Promise<MCPCallToolResponse> {
       }
     }
 
-    console.log(`[MCP] Tool called: ${tool.serverName} ${tool.name}`, resp)
+    console.log(`[MCP] Tool called: ${toolResponse.tool.serverName} ${toolResponse.tool.name}`, resp)
     return resp
   } catch (e) {
-    console.error(`[MCP] Error calling Tool: ${tool.serverName} ${tool.name}`, e)
+    console.error(`[MCP] Error calling Tool: ${toolResponse.tool.serverName} ${toolResponse.tool.name}`, e)
     return Promise.resolve({
       isError: true,
       content: [
         {
           type: 'text',
-          text: `Error calling tool ${tool.name}: ${e instanceof Error ? e.stack || e.message || 'No error details available' : JSON.stringify(e)}`
+          text: `Error calling tool ${toolResponse.tool.name}: ${e instanceof Error ? e.stack || e.message || 'No error details available' : JSON.stringify(e)}`
         }
       ]
     })
@@ -262,7 +314,7 @@ export function mcpToolsToAnthropicTools(mcpTools: MCPTool[]): Array<ToolUnion>
     const t: ToolUnion = {
       name: tool.id,
       description: tool.description,
-      // @ts-ignore no check
+      // @ts-ignore ignore type as it it unknow
       input_schema: tool.inputSchema
     }
     return t
@@ -275,53 +327,68 @@ export function anthropicToolUseToMcpTool(mcpTools: MCPTool[] | undefined, toolU
   if (!tool) {
     return undefined
   }
-  // @ts-ignore ignore type as it it unknow
-  tool.inputSchema = toolUse.input
   return tool
 }
 
-// export function mcpToolsToGeminiTools(mcpTools: MCPTool[] | undefined): geminiTool[] {
-//   if (!mcpTools || mcpTools.length === 0) {
-//     // No tools available
-//     return []
-//   }
-//   const functions: FunctionDeclaration[] = []
-
-//   for (const tool of mcpTools) {
-//     const properties = filterPropertieAttributes(tool, true)
-//     const functionDeclaration: FunctionDeclaration = {
-//       name: tool.id,
-//       description: tool.description,
-//       parameters: {
-//         type: SchemaType.OBJECT,
-//         properties:
-//           Object.keys(properties).length > 0
-//             ? Object.fromEntries(
-//                 Object.entries(properties).map(([key, value]) => [key, ensureValidSchema(value as Record<string, any>)])
-//               )
-//             : { _empty: { type: SchemaType.STRING } as SimpleStringSchema }
-//       } as FunctionDeclarationSchema
-//     }
-//     functions.push(functionDeclaration)
-//   }
-//   const tool: geminiTool = {
-//     functionDeclarations: functions
-//   }
-//   return [tool]
-// }
+/**
+ * @param mcpTools
+ * @returns
+ */
+export function mcpToolsToGeminiTools(mcpTools: MCPTool[]): Tool[] {
+  /**
+   * @typedef {import('@google/genai').Schema} Schema
+   */
+  const schemaKeys = [
+    'example',
+    'pattern',
+    'default',
+    'maxLength',
+    'minLength',
+    'minProperties',
+    'maxProperties',
+    'anyOf',
+    'description',
+    'enum',
+    'format',
+    'items',
+    'maxItems',
+    'maximum',
+    'minItems',
+    'minimum',
+    'nullable',
+    'properties',
+    'propertyOrdering',
+    'required',
+    'title',
+    'type'
+  ]
+  return [
+    {
+      functionDeclarations: mcpTools?.map((tool) => {
+        return {
+          name: tool.id,
+          description: tool.description,
+          parameters: {
+            type: GeminiSchemaType.OBJECT,
+            properties: filterProperties(tool.inputSchema, schemaKeys).properties,
+            required: tool.inputSchema.required
+          }
+        }
+      })
+    }
+  ]
+}
 
 export function geminiFunctionCallToMcpTool(
   mcpTools: MCPTool[] | undefined,
-  fcall: FunctionCall | undefined
+  toolCall: FunctionCall | undefined
 ): MCPTool | undefined {
-  if (!fcall) return undefined
+  if (!toolCall) return undefined
   if (!mcpTools) return undefined
-  const tool = mcpTools.find((tool) => tool.id === fcall.name)
+  const tool = mcpTools.find((tool) => tool.id === toolCall.name)
   if (!tool) {
     return undefined
   }
-  // @ts-ignore schema is not a valid property
-  tool.inputSchema = fcall.args
   return tool
 }
 
@@ -368,13 +435,13 @@ export function getMcpServerByTool(tool: MCPTool) {
   return servers.find((s) => s.id === tool.serverId)
 }
 
-export function parseToolUse(content: string, mcpTools: MCPTool[]): MCPToolResponse[] {
+export function parseToolUse(content: string, mcpTools: MCPTool[]): ToolUseResponse[] {
   if (!content || !mcpTools || mcpTools.length === 0) {
     return []
   }
   const toolUsePattern =
     /<tool_use>([\s\S]*?)<name>([\s\S]*?)<\/name>([\s\S]*?)<arguments>([\s\S]*?)<\/arguments>([\s\S]*?)<\/tool_use>/g
-  const tools: MCPToolResponse[] = []
+  const tools: ToolUseResponse[] = []
   let match
   let idx = 0
   // Find all tool use blocks
@@ -401,10 +468,9 @@ export function parseToolUse(content: string, mcpTools: MCPTool[]): MCPToolRespo
     // Add to tools array
     tools.push({
       id: `${toolName}-${idx++}`, // Unique ID for each tool use
-      tool: {
-        ...mcpTool,
-        inputSchema: parsedArgs
-      },
+      toolUseId: mcpTool.id,
+      tool: mcpTool,
+      arguments: parsedArgs,
       status: 'pending'
     })
 
@@ -414,36 +480,69 @@ export function parseToolUse(content: string, mcpTools: MCPTool[]): MCPToolRespo
   return tools
 }
 
-export async function parseAndCallTools(
-  content: string,
-  toolResponses: MCPToolResponse[],
+export async function parseAndCallTools<R>(
+  tools: MCPToolResponse[],
+  allToolResponses: MCPToolResponse[],
   onChunk: CompletionsParams['onChunk'],
-  idx: number,
-  convertToMessage: (
-    toolCallId: string,
-    resp: MCPCallToolResponse,
-    isVisionModel: boolean
-  ) => ChatCompletionMessageParam | MessageParam | Content | OpenAI.Responses.EasyInputMessage,
-  mcpTools?: MCPTool[],
-  isVisionModel: boolean = false
-): Promise<(ChatCompletionMessageParam | MessageParam | Content | OpenAI.Responses.EasyInputMessage)[]> {
-  const toolResults: (ChatCompletionMessageParam | MessageParam | Content | OpenAI.Responses.EasyInputMessage)[] = []
-  // process tool use
-  const tools = parseToolUse(content, mcpTools || [])
-  if (!tools || tools.length === 0) {
+  convertToMessage: (mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model) => R | undefined,
+  model: Model,
+  mcpTools?: MCPTool[]
+): Promise<
+  (ChatCompletionMessageParam | MessageParam | Content | OpenAI.Responses.ResponseInputItem | ToolResultBlockParam)[]
+>
+
+export async function parseAndCallTools<R>(
+  content: string,
+  allToolResponses: MCPToolResponse[],
+  onChunk: CompletionsParams['onChunk'],
+  convertToMessage: (mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model) => R | undefined,
+  model: Model,
+  mcpTools?: MCPTool[]
+): Promise<
+  (ChatCompletionMessageParam | MessageParam | Content | OpenAI.Responses.ResponseInputItem | ToolResultBlockParam)[]
+>
+
+export async function parseAndCallTools<R>(
+  content: string | MCPToolResponse[],
+  allToolResponses: MCPToolResponse[],
+  onChunk: CompletionsParams['onChunk'],
+  convertToMessage: (mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model) => R | undefined,
+  model: Model,
+  mcpTools?: MCPTool[]
+): Promise<R[]> {
+  const toolResults: R[] = []
+  let curToolResponses: MCPToolResponse[] = []
+  if (Array.isArray(content)) {
+    curToolResponses = content
+  } else {
+    // process tool use
+    curToolResponses = parseToolUse(content, mcpTools || [])
+  }
+  if (!curToolResponses || curToolResponses.length === 0) {
     return toolResults
   }
-  for (let i = 0; i < tools.length; i++) {
-    const tool = tools[i]
-    upsertMCPToolResponse(toolResponses, { id: `${tool.id}-${idx}-${i}`, tool: tool.tool, status: 'invoking' }, onChunk)
+  for (let i = 0; i < curToolResponses.length; i++) {
+    const toolResponse = curToolResponses[i]
+    upsertMCPToolResponse(
+      allToolResponses,
+      {
+        ...toolResponse,
+        status: 'invoking'
+      },
+      onChunk
+    )
   }
 
-  const toolPromises = tools.map(async (tool, i) => {
+  const toolPromises = curToolResponses.map(async (toolResponse) => {
     const images: string[] = []
-    const toolCallResponse = await callMCPTool(tool.tool)
+    const toolCallResponse = await callMCPTool(toolResponse)
     upsertMCPToolResponse(
-      toolResponses,
-      { id: `${tool.id}-${idx}-${i}`, tool: tool.tool, status: 'done', response: toolCallResponse },
+      allToolResponses,
+      {
+        ...toolResponse,
+        status: 'done',
+        response: toolCallResponse
+      },
       onChunk
     )
 
@@ -466,15 +565,15 @@ export async function parseAndCallTools(
       })
     }
 
-    return convertToMessage(tool.tool.id, toolCallResponse, isVisionModel)
+    return convertToMessage(toolResponse, toolCallResponse, model)
   })
 
-  toolResults.push(...(await Promise.all(toolPromises)))
+  toolResults.push(...(await Promise.all(toolPromises)).filter((t) => typeof t !== 'undefined'))
   return toolResults
 }
 
 export function mcpToolCallResponseToOpenAICompatibleMessage(
-  toolCallId: string,
+  mcpToolResponse: MCPToolResponse,
   resp: MCPCallToolResponse,
   isVisionModel: boolean = false
 ): ChatCompletionMessageParam {
@@ -488,7 +587,7 @@ export function mcpToolCallResponseToOpenAICompatibleMessage(
     const content: ChatCompletionContentPart[] = [
       {
         type: 'text',
-        text: `Here is the result of tool call ${toolCallId}:`
+        text: `Here is the result of mcp tool use \`${mcpToolResponse.tool.name}\`:`
       }
     ]
 
@@ -541,7 +640,7 @@ export function mcpToolCallResponseToOpenAICompatibleMessage(
 }
 
 export function mcpToolCallResponseToOpenAIMessage(
-  toolCallId: string,
+  mcpToolResponse: MCPToolResponse,
   resp: MCPCallToolResponse,
   isVisionModel: boolean = false
 ): OpenAI.Responses.EasyInputMessage {
@@ -555,7 +654,7 @@ export function mcpToolCallResponseToOpenAIMessage(
     const content: OpenAI.Responses.ResponseInputContent[] = [
       {
         type: 'input_text',
-        text: `Here is the result of tool call ${toolCallId}:`
+        text: `Here is the result of mcp tool use \`${mcpToolResponse.tool.name}\`:`
       }
     ]
 
@@ -597,9 +696,9 @@ export function mcpToolCallResponseToOpenAIMessage(
 }
 
 export function mcpToolCallResponseToAnthropicMessage(
-  toolCallId: string,
+  mcpToolResponse: MCPToolResponse,
   resp: MCPCallToolResponse,
-  isVisionModel: boolean = false
+  model: Model
 ): MessageParam {
   const message = {
     role: 'user'
@@ -610,10 +709,10 @@ export function mcpToolCallResponseToAnthropicMessage(
     const content: ContentBlockParam[] = [
       {
         type: 'text',
-        text: `Here is the result of tool call ${toolCallId}:`
+        text: `Here is the result of mcp tool use \`${mcpToolResponse.tool.name}\`:`
       }
     ]
-    if (isVisionModel) {
+    if (isVisionModel(model)) {
       for (const item of resp.content) {
         switch (item.type) {
           case 'text':
@@ -665,7 +764,7 @@ export function mcpToolCallResponseToAnthropicMessage(
 }
 
 export function mcpToolCallResponseToGeminiMessage(
-  toolCallId: string,
+  mcpToolResponse: MCPToolResponse,
   resp: MCPCallToolResponse,
   isVisionModel: boolean = false
 ): Content {
@@ -682,7 +781,7 @@ export function mcpToolCallResponseToGeminiMessage(
   } else {
     const parts: Part[] = [
       {
-        text: `Here is the result of tool call ${toolCallId}:`
+        text: `Here is the result of mcp tool use \`${mcpToolResponse.tool.name}\`:`
       }
     ]
     if (isVisionModel) {
diff --git a/src/renderer/src/utils/prompt.ts b/src/renderer/src/utils/prompt.ts
index 698fdb1186..d014bd4ce0 100644
--- a/src/renderer/src/utils/prompt.ts
+++ b/src/renderer/src/utils/prompt.ts
@@ -147,7 +147,7 @@ ${availableTools}
 </tools>`
 }
 
-export const buildSystemPrompt = (userSystemPrompt: string, tools: MCPTool[]): string => {
+export const buildSystemPrompt = (userSystemPrompt: string, tools?: MCPTool[]): string => {
   if (tools && tools.length > 0) {
     return SYSTEM_PROMPT.replace('{{ USER_SYSTEM_PROMPT }}', userSystemPrompt)
       .replace('{{ TOOL_USE_EXAMPLES }}', ToolUseExamples)