feat: add test case for mcp response in apiservice (#8300)

* refactor: 将工具调用逻辑移动到中间件文件

* feat(日志): 在流处理中添加调试日志记录

添加对分块数据的调试日志记录，便于跟踪流处理过程中的数据流动

* test(api-service): 添加工具调用响应测试用例

src/renderer/src/aiCore/clients/gemini/GeminiAPIClient.ts

@@ -561,6 +561,7 @@ export class GeminiAPIClient extends BaseApiClient<
     let isFirstThinkingChunk = true
     return () => ({
       async transform(chunk: GeminiSdkRawChunk, controller: TransformStreamDefaultController<GenericChunk>) {
+        logger.silly('chunk', chunk)
         if (chunk.candidates && chunk.candidates.length > 0) {
           for (const candidate of chunk.candidates) {
             if (candidate.content) {

src/renderer/src/aiCore/middleware/common/FinalChunkConsumerMiddleware.ts

@@ -64,6 +64,7 @@ const FinalChunkConsumerMiddleware: CompletionsMiddleware =
         try {
           while (true) {
             const { done, value: chunk } = await reader.read()
+            logger.silly('chunk', chunk)
             if (done) {
               logger.debug(`Input stream finished.`)
               break

src/renderer/src/aiCore/middleware/core/McpToolChunkMiddleware.ts

@@ -1,8 +1,15 @@
 import { loggerService } from '@logger'
-import { MCPTool, MCPToolResponse, Model, ToolCallResponse } from '@renderer/types'
+import { MCPCallToolResponse, MCPTool, MCPToolResponse, Model, ToolCallResponse } from '@renderer/types'
 import { ChunkType, MCPToolCreatedChunk } from '@renderer/types/chunk'
 import { SdkMessageParam, SdkRawOutput, SdkToolCall } from '@renderer/types/sdk'
-import { parseAndCallTools } from '@renderer/utils/mcp-tools'
+import {
+  callMCPTool,
+  getMcpServerByTool,
+  isToolAutoApproved,
+  parseToolUse,
+  upsertMCPToolResponse
+} from '@renderer/utils/mcp-tools'
+import { confirmSameNameTools, requestToolConfirmation, setToolIdToNameMapping } from '@renderer/utils/userConfirmation'
 
 import { CompletionsParams, CompletionsResult, GenericChunk } from '../schemas'
 import { CompletionsContext, CompletionsMiddleware } from '../types'
@@ -369,4 +376,207 @@ function getCurrentReqMessages(ctx: CompletionsContext): SdkMessageParam[] {
   return ctx.apiClientInstance.extractMessagesFromSdkPayload(sdkPayload)
 }
 
-export default McpToolChunkMiddleware
+export async function parseAndCallTools<R>(
+  tools: MCPToolResponse[],
+  allToolResponses: MCPToolResponse[],
+  onChunk: CompletionsParams['onChunk'],
+  convertToMessage: (mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model) => R | undefined,
+  model: Model,
+  mcpTools?: MCPTool[],
+  abortSignal?: AbortSignal,
+  topicId?: CompletionsParams['topicId']
+): Promise<{ toolResults: R[]; confirmedToolResponses: MCPToolResponse[] }>
+
+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[],
+  abortSignal?: AbortSignal,
+  topicId?: CompletionsParams['topicId']
+): Promise<{ toolResults: R[]; confirmedToolResponses: MCPToolResponse[] }>
+
+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[],
+  abortSignal?: AbortSignal,
+  topicId?: CompletionsParams['topicId']
+): Promise<{ toolResults: R[]; confirmedToolResponses: MCPToolResponse[] }> {
+  const toolResults: R[] = []
+  let curToolResponses: MCPToolResponse[] = []
+  if (Array.isArray(content)) {
+    curToolResponses = content
+  } else {
+    // process tool use
+    curToolResponses = parseToolUse(content, mcpTools || [], 0)
+  }
+  if (!curToolResponses || curToolResponses.length === 0) {
+    return { toolResults, confirmedToolResponses: [] }
+  }
+
+  for (const toolResponse of curToolResponses) {
+    upsertMCPToolResponse(
+      allToolResponses,
+      {
+        ...toolResponse,
+        status: 'pending'
+      },
+      onChunk!
+    )
+  }
+
+  // 创建工具确认Promise映射，并立即处理每个确认
+  const confirmedTools: MCPToolResponse[] = []
+  const pendingPromises: Promise<void>[] = []
+
+  curToolResponses.forEach((toolResponse) => {
+    const server = getMcpServerByTool(toolResponse.tool)
+    const isAutoApproveEnabled = isToolAutoApproved(toolResponse.tool, server)
+    let confirmationPromise: Promise<boolean>
+    if (isAutoApproveEnabled) {
+      confirmationPromise = Promise.resolve(true)
+    } else {
+      setToolIdToNameMapping(toolResponse.id, toolResponse.tool.name)
+
+      confirmationPromise = requestToolConfirmation(toolResponse.id, abortSignal).then((confirmed) => {
+        if (confirmed && server) {
+          // 自动确认其他同名的待确认工具
+          confirmSameNameTools(toolResponse.tool.name)
+        }
+        return confirmed
+      })
+    }
+
+    const processingPromise = confirmationPromise
+      .then(async (confirmed) => {
+        if (confirmed) {
+          // 立即更新为invoking状态
+          upsertMCPToolResponse(
+            allToolResponses,
+            {
+              ...toolResponse,
+              status: 'invoking'
+            },
+            onChunk!
+          )
+
+          // 执行工具调用
+          try {
+            const images: string[] = []
+            const toolCallResponse = await callMCPTool(toolResponse, topicId, model.name)
+
+            // 立即更新为done状态
+            upsertMCPToolResponse(
+              allToolResponses,
+              {
+                ...toolResponse,
+                status: 'done',
+                response: toolCallResponse
+              },
+              onChunk!
+            )
+
+            // 处理图片
+            for (const content of toolCallResponse.content) {
+              if (content.type === 'image' && content.data) {
+                images.push(`data:${content.mimeType};base64,${content.data}`)
+              }
+            }
+
+            if (images.length) {
+              onChunk?.({
+                type: ChunkType.IMAGE_CREATED
+              })
+              onChunk?.({
+                type: ChunkType.IMAGE_COMPLETE,
+                image: {
+                  type: 'base64',
+                  images: images
+                }
+              })
+            }
+
+            // 转换消息并添加到结果
+            const convertedMessage = convertToMessage(toolResponse, toolCallResponse, model)
+            if (convertedMessage) {
+              confirmedTools.push(toolResponse)
+              toolResults.push(convertedMessage)
+            }
+          } catch (error) {
+            logger.error(`Error executing tool ${toolResponse.id}:`, error)
+            // 更新为错误状态
+            upsertMCPToolResponse(
+              allToolResponses,
+              {
+                ...toolResponse,
+                status: 'done',
+                response: {
+                  isError: true,
+                  content: [
+                    {
+                      type: 'text',
+                      text: `Error executing tool: ${error instanceof Error ? error.message : 'Unknown error'}`
+                    }
+                  ]
+                }
+              },
+              onChunk!
+            )
+          }
+        } else {
+          // 立即更新为cancelled状态
+          upsertMCPToolResponse(
+            allToolResponses,
+            {
+              ...toolResponse,
+              status: 'cancelled',
+              response: {
+                isError: false,
+                content: [
+                  {
+                    type: 'text',
+                    text: 'Tool call cancelled by user.'
+                  }
+                ]
+              }
+            },
+            onChunk!
+          )
+        }
+      })
+      .catch((error) => {
+        logger.error(`Error waiting for tool confirmation ${toolResponse.id}:`, error)
+        // 立即更新为cancelled状态
+        upsertMCPToolResponse(
+          allToolResponses,
+          {
+            ...toolResponse,
+            status: 'cancelled',
+            response: {
+              isError: true,
+              content: [
+                {
+                  type: 'text',
+                  text: `Error in confirmation process: ${error instanceof Error ? error.message : 'Unknown error'}`
+                }
+              ]
+            }
+          },
+          onChunk!
+        )
+      })
+
+    pendingPromises.push(processingPromise)
+  })
+
+  // 等待所有工具处理完成（但每个工具的状态已经实时更新）
+  await Promise.all(pendingPromises)
+
+  return { toolResults, confirmedToolResponses: confirmedTools }
+}

src/renderer/src/services/__tests__/ApiService.test.ts

@@ -6,8 +6,10 @@ import { ApiClientFactory } from '@renderer/aiCore/clients/ApiClientFactory'
 import { BaseApiClient } from '@renderer/aiCore/clients/BaseApiClient'
 import { GeminiAPIClient } from '@renderer/aiCore/clients/gemini/GeminiAPIClient'
 import { GenericChunk } from '@renderer/aiCore/middleware/schemas'
-import { Assistant, Provider, WebSearchSource } from '@renderer/types'
+import { isVisionModel } from '@renderer/config/models'
+import { Assistant, MCPCallToolResponse, MCPToolResponse, Model, Provider, WebSearchSource } from '@renderer/types'
 import {
+  Chunk,
   ChunkType,
   LLMResponseCompleteChunk,
   LLMWebSearchCompleteChunk,
@@ -15,7 +17,15 @@ import {
   TextStartChunk,
   ThinkingStartChunk
 } from '@renderer/types/chunk'
-import { GeminiSdkRawChunk, OpenAISdkRawChunk, OpenAISdkRawContentSource } from '@renderer/types/sdk'
+import {
+  GeminiSdkMessageParam,
+  GeminiSdkRawChunk,
+  GeminiSdkToolCall,
+  OpenAISdkRawChunk,
+  OpenAISdkRawContentSource
+} from '@renderer/types/sdk'
+import * as McpToolsModule from '@renderer/utils/mcp-tools'
+import { mcpToolCallResponseToGeminiMessage } from '@renderer/utils/mcp-tools'
 import { cloneDeep } from 'lodash'
 import OpenAI from 'openai'
 import { ChatCompletionChunk } from 'openai/resources'
@@ -610,11 +620,32 @@ const geminiToolUseChunks: GeminiSdkRawChunk[] = [
           ],
           role: 'model'
         },
-        finishReason: FinishReason.STOP,
         index: 0
       }
     ],
     usageMetadata: {}
+  } as GeminiSdkRawChunk,
+  {
+    candidates: [
+      {
+        content: {
+          parts: [
+            {
+              functionCall: {
+                name: 'mcp-tool-1',
+                args: {
+                  name: 'alice',
+                  age: 13
+                }
+              } as GeminiSdkToolCall
+            }
+          ],
+          role: 'model'
+        },
+        finishReason: FinishReason.STOP
+      }
+    ],
+    usageMetadata: {}
   } as GeminiSdkRawChunk
 ]
 
@@ -895,6 +926,7 @@ const mockOpenaiApiClient = {
               choice.delta &&
               Object.keys(choice.delta).length > 0 &&
               (!('content' in choice.delta) ||
+                (choice.delta.tool_calls && choice.delta.tool_calls.length > 0) ||
                 (typeof choice.delta.content === 'string' && choice.delta.content !== '') ||
                 (typeof (choice.delta as any).reasoning_content === 'string' &&
                   (choice.delta as any).reasoning_content !== '') ||
@@ -1141,6 +1173,14 @@ mockGeminiThinkingApiClient.createCompletions = vi.fn().mockImplementation(() =>
 
 const mockGeminiToolUseApiClient = cloneDeep(mockGeminiApiClient)
 mockGeminiToolUseApiClient.createCompletions = vi.fn().mockImplementation(() => geminiToolUseChunkGenerator())
+mockGeminiToolUseApiClient.convertMcpToolResponseToSdkMessageParam = vi
+  .fn()
+  .mockImplementation(
+    (mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model): GeminiSdkMessageParam | undefined => {
+      // mcp使用tooluse
+      return mcpToolCallResponseToGeminiMessage(mcpToolResponse, resp, isVisionModel(model))
+    }
+  )
 
 const mockProvider = {
   id: 'gemini',
@@ -1537,199 +1577,235 @@ describe('ApiService', () => {
     expect(filteredChunks).toEqual(expectedChunks)
   })
 
-  // it('should extract tool use responses correctly', async () => {
+  it('should extract tool use responses correctly', async () => {
-  //   const mockCreate = vi.mocked(ApiClientFactory.create)
+    const mockCreate = vi.mocked(ApiClientFactory.create)
-  //   mockCreate.mockReturnValue(mockGeminiToolUseApiClient as unknown as BaseApiClient)
+    mockCreate.mockReturnValue(mockGeminiToolUseApiClient as unknown as BaseApiClient)
-  //   const AI = new AiProvider(mockProvider)
+    const AI = new AiProvider(mockProvider)
-  //   const spy = vi.spyOn(McpToolsModule, 'callMCPTool')
-  //   spy.mockResolvedValue({
-  //     content: [{ type: 'text', text: 'test' }],
-  //     isError: false
-  //   })
 
-  //   const result = await AI.completions({
+    const mcpChunks: GenericChunk[] = []
-  //     callType: 'test',
+    const firstResponseChunks: GenericChunk[] = []
-  //     messages: [],
-  //     assistant: {
-  //       id: '1',
-  //       name: 'test',
-  //       prompt: 'test',
-  //       model: {
-  //         id: 'gemini-2.5-pro',
-  //         name: 'Gemini 2.5 Pro'
-  //       },
-  //       settings: {
-  //         toolUseMode: 'prompt'
-  //       }
-  //     } as Assistant,
-  //     mcpTools: [
-  //       {
-  //         id: 'mcp-tool-1',
-  //         name: 'mcp-tool-1',
-  //         serverId: 'mcp-server-1',
-  //         serverName: 'mcp-server-1',
-  //         description: 'mcp-tool-1',
-  //         inputSchema: {
-  //           type: 'object',
-  //           title: 'mcp-tool-1',
-  //           properties: {
-  //             name: { type: 'string' },
-  //             age: { type: 'number' }
-  //           },
-  //           description: 'print the name and age',
-  //           required: ['name', 'age']
-  //         }
-  //       }
-  //     ],
-  //     onChunk: mockOnChunk,
-  //     enableReasoning: true,
-  //     streamOutput: true
-  //   })
 
-  //   expect(result).toBeDefined()
+    const spy = vi.spyOn(McpToolsModule, 'callMCPTool')
-  //   expect(ApiClientFactory.create).toHaveBeenCalledWith(mockProvider)
+    spy.mockResolvedValue({
-  //   expect(result.stream).toBeDefined()
+      content: [{ type: 'text', text: 'test' }],
+      isError: false
+    })
 
-  //   const stream = result.stream! as ReadableStream<GenericChunk>
+    const onChunk = vi.fn((chunk: Chunk) => {
-  //   const reader = stream.getReader()
+      mcpChunks.push(chunk)
+    })
 
-  //   const chunks: GenericChunk[] = []
+    const result = await AI.completions({
+      callType: 'test',
+      messages: [],
+      assistant: {
+        id: '1',
+        name: 'test',
+        prompt: 'test',
+        model: {
+          id: 'gemini-2.5-pro',
+          name: 'Gemini 2.5 Pro'
+        },
+        settings: {
+          toolUseMode: 'prompt'
+        }
+      } as Assistant,
+      mcpTools: [
+        {
+          id: 'mcp-tool-1',
+          name: 'mcp-tool-1',
+          serverId: 'mcp-server-1',
+          serverName: 'mcp-server-1',
+          description: 'mcp-tool-1',
+          inputSchema: {
+            type: 'object',
+            title: 'mcp-tool-1',
+            properties: {
+              name: { type: 'string' },
+              age: { type: 'number' }
+            },
+            description: 'print the name and age',
+            required: ['name', 'age']
+          }
+        }
+      ],
+      onChunk,
+      enableReasoning: true,
+      streamOutput: true
+    })
 
-  //   while (true) {
+    expect(result).toBeDefined()
-  //     const { done, value } = await reader.read()
+    expect(ApiClientFactory.create).toHaveBeenCalledWith(mockProvider)
-  //     if (done) break
+    expect(result.stream).toBeDefined()
-  //     chunks.push(value)
-  //   }
 
-  //   reader.releaseLock()
+    const stream = result.stream! as ReadableStream<GenericChunk>
+    const reader = stream.getReader()
 
-  //   const filteredChunks = chunks.map((chunk) => {
+    while (true) {
-  //     if (chunk.type === ChunkType.THINKING_DELTA || chunk.type === ChunkType.THINKING_COMPLETE) {
+      const { done, value: chunk } = await reader.read()
-  //       delete (chunk as any).thinking_millsec
+      if (done) break
-  //       return chunk
+      firstResponseChunks.push(chunk)
-  //     }
+    }
-  //     return chunk
-  //   })
 
-  //   const expectedChunks: GenericChunk[] = [
+    reader.releaseLock()
-  //     {
-  //       type: ChunkType.THINKING_START
-  //     },
-  //     {
-  //       type: ChunkType.THINKING_DELTA,
-  //       text: '**Initiating File Retrieval**\n\nI\'ve determined that the `tool_mcp-tool-1` tool is suitable for this task. It seems the user intends to read a file, and this tool aligns with that objective. Currently, I\'m focusing on the necessary parameters. The `tool_mcp-tool-1` tool requires a `name` and `age`, which the user has helpfully provided: `{"name": "xxx", "age": 20}`. I\'m verifying the input.\n\n\n'
-  //     },
-  //     {
-  //       type: ChunkType.THINKING_COMPLETE,
-  //       text: '**Initiating File Retrieval**\n\nI\'ve determined that the `tool_mcp-tool-1` tool is suitable for this task. It seems the user intends to read a file, and this tool aligns with that objective. Currently, I\'m focusing on the necessary parameters. The `tool_mcp-tool-1` tool requires a `name` and `age`, which the user has helpfully provided: `{"name": "xxx", "age": 20}`. I\'m verifying the input.\n\n\n'
-  //     },
-  //     {
-  //       type: ChunkType.TEXT_START
-  //     },
-  //     {
-  //       type: ChunkType.TEXT_DELTA,
-  //       text: '好的，我将为您打印用户的'
-  //     },
-  //     {
-  //       type: ChunkType.TEXT_DELTA,
-  //       text: '好的，我将为您打印用户的信息。\n'
-  //     },
-  //     {
-  //       type: ChunkType.TEXT_COMPLETE,
-  //       text: '好的，我将为您打印用户的信息。\n'
-  //     },
-  //     {
-  //       type: ChunkType.MCP_TOOL_CREATED
-  //     },
-  //     {
-  //       type: ChunkType.MCP_TOOL_PENDING,
-  //       responses: [
-  //         {
-  //           id: 'mcp-tool-1',
-  //           tool: {
-  //             id: 'mcp-tool-1',
-  //             serverId: 'mcp-server-1',
-  //             serverName: 'mcp-server-1',
-  //             name: 'mcp-tool-1',
-  //             inputSchema: {
-  //               type: 'object',
-  //               title: 'mcp-tool-1',
-  //               properties: {
-  //                 name: { type: 'string' },
-  //                 age: { type: 'number' }
-  //               },
-  //               description: 'print the name and age',
-  //               required: ['name', 'age']
-  //             }
-  //           },
-  //           arguments: {
-  //             name: 'xxx',
-  //             age: 20
-  //           },
-  //           status: 'pending'
-  //         }
-  //       ]
-  //     },
-  //     {
-  //       type: ChunkType.MCP_TOOL_IN_PROGRESS,
-  //       responses: [
-  //         {
-  //           id: 'mcp-tool-1',
-  //           tool: {
-  //             id: 'mcp-tool-1',
-  //             serverId: 'mcp-server-1',
-  //             serverName: 'mcp-server-1',
-  //             name: 'mcp-tool-1',
-  //             inputSchema: {
-  //               type: 'object',
-  //               title: 'mcp-tool-1',
-  //               properties: {
-  //                 name: { type: 'string' },
-  //                 age: { type: 'number' }
-  //               },
-  //               description: 'print the name and age',
-  //               required: ['name', 'age']
-  //             }
-  //           },
-  //           arguments: {
-  //             name: 'xxx',
-  //             age: 20
-  //           },
-  //           status: 'invoking'
-  //         }
-  //       ]
-  //     },
-  //     {
-  //       type: ChunkType.MCP_TOOL_COMPLETE,
-  //       responses: [
-  //         {
-  //           id: 'mcp-tool-1',
-  //           tool: {
-  //             id: 'mcp-tool-1',
-  //             serverId: 'mcp-server-1',
-  //             serverName: 'mcp-server-1',
-  //             name: 'mcp-tool-1',
-  //             inputSchema: {
-  //               type: 'object',
-  //               title: 'mcp-tool-1',
-  //               properties: {
-  //                 name: { type: 'string' },
-  //                 age: { type: 'number' }
-  //               },
-  //               description: 'print the name and age',
-  //               required: ['name', 'age']
-  //             }
-  //           },
-  //           arguments: {
-  //             name: 'xxx',
-  //             age: 20
-  //           },
-  //           status: 'done'
-  //         }
-  //       ]
-  //     }
-  //   ]
 
-  //   expect(filteredChunks).toEqual(expectedChunks)
+    const filteredFirstResponseChunks = firstResponseChunks.map((chunk) => {
-  // })
+      if (chunk.type === ChunkType.THINKING_DELTA || chunk.type === ChunkType.THINKING_COMPLETE) {
+        delete (chunk as any).thinking_millsec
+        return chunk
+      }
+      return chunk
+    })
+
+    const expectedFirstResponseChunks: GenericChunk[] = [
+      {
+        type: ChunkType.THINKING_START
+      },
+      {
+        type: ChunkType.THINKING_DELTA,
+        text: '**Initiating File Retrieval**\n\nI\'ve determined that the `tool_mcp-tool-1` tool is suitable for this task. It seems the user intends to read a file, and this tool aligns with that objective. Currently, I\'m focusing on the necessary parameters. The `tool_mcp-tool-1` tool requires a `name` and `age`, which the user has helpfully provided: `{"name": "xxx", "age": 20}`. I\'m verifying the input.\n\n\n'
+      },
+      {
+        type: ChunkType.THINKING_COMPLETE,
+        text: '**Initiating File Retrieval**\n\nI\'ve determined that the `tool_mcp-tool-1` tool is suitable for this task. It seems the user intends to read a file, and this tool aligns with that objective. Currently, I\'m focusing on the necessary parameters. The `tool_mcp-tool-1` tool requires a `name` and `age`, which the user has helpfully provided: `{"name": "xxx", "age": 20}`. I\'m verifying the input.\n\n\n'
+      },
+      {
+        type: ChunkType.TEXT_START
+      },
+      {
+        type: ChunkType.TEXT_DELTA,
+        text: '好的，我将为您打印用户的'
+      },
+      {
+        type: ChunkType.TEXT_DELTA,
+        text: '好的，我将为您打印用户的信息。\n'
+      },
+      {
+        type: ChunkType.TEXT_COMPLETE,
+        text: '好的，我将为您打印用户的信息。\n'
+      },
+      {
+        type: ChunkType.LLM_RESPONSE_COMPLETE,
+        response: {
+          usage: {
+            completion_tokens: 0,
+            prompt_tokens: 0,
+            total_tokens: 0
+          }
+        }
+      }
+    ]
+
+    const expectedMcpResponseChunks: GenericChunk[] = [
+      {
+        type: ChunkType.MCP_TOOL_PENDING,
+        responses: [
+          {
+            id: 'mcp-tool-1-0',
+            tool: {
+              description: 'mcp-tool-1',
+              id: 'mcp-tool-1',
+              serverId: 'mcp-server-1',
+              serverName: 'mcp-server-1',
+              name: 'mcp-tool-1',
+              inputSchema: {
+                type: 'object',
+                title: 'mcp-tool-1',
+                properties: {
+                  name: { type: 'string' },
+                  age: { type: 'number' }
+                },
+                description: 'print the name and age',
+                required: ['name', 'age']
+              }
+            },
+            toolUseId: 'mcp-tool-1',
+            arguments: {
+              name: 'xxx',
+              age: 20
+            },
+            status: 'pending'
+          }
+        ]
+      },
+      {
+        type: ChunkType.MCP_TOOL_IN_PROGRESS,
+        responses: [
+          {
+            id: 'mcp-tool-1-0',
+            response: undefined,
+            tool: {
+              description: 'mcp-tool-1',
+              id: 'mcp-tool-1',
+              serverId: 'mcp-server-1',
+              serverName: 'mcp-server-1',
+              name: 'mcp-tool-1',
+              inputSchema: {
+                type: 'object',
+                title: 'mcp-tool-1',
+                properties: {
+                  name: { type: 'string' },
+                  age: { type: 'number' }
+                },
+                description: 'print the name and age',
+                required: ['name', 'age']
+              }
+            },
+            toolUseId: 'mcp-tool-1',
+            arguments: {
+              name: 'xxx',
+              age: 20
+            },
+            status: 'invoking'
+          }
+        ]
+      },
+      {
+        type: ChunkType.MCP_TOOL_COMPLETE,
+        responses: [
+          {
+            id: 'mcp-tool-1-0',
+            tool: {
+              description: 'mcp-tool-1',
+              id: 'mcp-tool-1',
+              serverId: 'mcp-server-1',
+              serverName: 'mcp-server-1',
+              name: 'mcp-tool-1',
+              inputSchema: {
+                type: 'object',
+                title: 'mcp-tool-1',
+                properties: {
+                  name: { type: 'string' },
+                  age: { type: 'number' }
+                },
+                description: 'print the name and age',
+                required: ['name', 'age']
+              }
+            },
+            response: {
+              content: [
+                {
+                  text: 'test',
+                  type: 'text'
+                }
+              ],
+              isError: false
+            },
+            toolUseId: 'mcp-tool-1',
+            arguments: {
+              name: 'xxx',
+              age: 20
+            },
+            status: 'done'
+          }
+        ]
+      },
+      {
+        type: ChunkType.LLM_RESPONSE_CREATED
+      }
+    ]
+
+    expect(filteredFirstResponseChunks).toEqual(expectedFirstResponseChunks)
+    expect(mcpChunks).toEqual(expectedMcpResponseChunks)
+  })
 })

src/renderer/src/utils/mcp-tools.ts

@@ -27,9 +27,6 @@ import {
   ChatCompletionTool
 } from 'openai/resources'
 
-import { CompletionsParams } from '../aiCore/middleware/schemas'
-import { confirmSameNameTools, requestToolConfirmation, setToolIdToNameMapping } from './userConfirmation'
-
 const logger = loggerService.withContext('Utils:MCPTools')
 
 const MCP_AUTO_INSTALL_SERVER_NAME = '@cherry/mcp-auto-install'
@@ -534,211 +531,6 @@ export function parseToolUse(content: string, mcpTools: MCPTool[], startIdx: num
   return tools
 }
 
-export async function parseAndCallTools<R>(
-  tools: MCPToolResponse[],
-  allToolResponses: MCPToolResponse[],
-  onChunk: CompletionsParams['onChunk'],
-  convertToMessage: (mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model) => R | undefined,
-  model: Model,
-  mcpTools?: MCPTool[],
-  abortSignal?: AbortSignal,
-  topicId?: CompletionsParams['topicId']
-): Promise<{ toolResults: R[]; confirmedToolResponses: MCPToolResponse[] }>
-
-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[],
-  abortSignal?: AbortSignal,
-  topicId?: CompletionsParams['topicId']
-): Promise<{ toolResults: R[]; confirmedToolResponses: MCPToolResponse[] }>
-
-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[],
-  abortSignal?: AbortSignal,
-  topicId?: CompletionsParams['topicId']
-): Promise<{ toolResults: R[]; confirmedToolResponses: MCPToolResponse[] }> {
-  const toolResults: R[] = []
-  let curToolResponses: MCPToolResponse[] = []
-  if (Array.isArray(content)) {
-    curToolResponses = content
-  } else {
-    // process tool use
-    curToolResponses = parseToolUse(content, mcpTools || [], 0)
-  }
-  if (!curToolResponses || curToolResponses.length === 0) {
-    return { toolResults, confirmedToolResponses: [] }
-  }
-
-  for (const toolResponse of curToolResponses) {
-    upsertMCPToolResponse(
-      allToolResponses,
-      {
-        ...toolResponse,
-        status: 'pending'
-      },
-      onChunk!
-    )
-  }
-
-  // 创建工具确认Promise映射，并立即处理每个确认
-  const confirmedTools: MCPToolResponse[] = []
-  const pendingPromises: Promise<void>[] = []
-
-  curToolResponses.forEach((toolResponse) => {
-    const server = getMcpServerByTool(toolResponse.tool)
-    const isAutoApproveEnabled = isToolAutoApproved(toolResponse.tool, server)
-    let confirmationPromise: Promise<boolean>
-    if (isAutoApproveEnabled) {
-      confirmationPromise = Promise.resolve(true)
-    } else {
-      setToolIdToNameMapping(toolResponse.id, toolResponse.tool.name)
-
-      confirmationPromise = requestToolConfirmation(toolResponse.id, abortSignal).then((confirmed) => {
-        if (confirmed && server) {
-          // 自动确认其他同名的待确认工具
-          confirmSameNameTools(toolResponse.tool.name)
-        }
-        return confirmed
-      })
-    }
-
-    const processingPromise = confirmationPromise
-      .then(async (confirmed) => {
-        if (confirmed) {
-          // 立即更新为invoking状态
-          upsertMCPToolResponse(
-            allToolResponses,
-            {
-              ...toolResponse,
-              status: 'invoking'
-            },
-            onChunk!
-          )
-
-          // 执行工具调用
-          try {
-            const images: string[] = []
-            const toolCallResponse = await callMCPTool(toolResponse, topicId, model.name)
-
-            // 立即更新为done状态
-            upsertMCPToolResponse(
-              allToolResponses,
-              {
-                ...toolResponse,
-                status: 'done',
-                response: toolCallResponse
-              },
-              onChunk!
-            )
-
-            // 处理图片
-            for (const content of toolCallResponse.content) {
-              if (content.type === 'image' && content.data) {
-                images.push(`data:${content.mimeType};base64,${content.data}`)
-              }
-            }
-
-            if (images.length) {
-              onChunk?.({
-                type: ChunkType.IMAGE_CREATED
-              })
-              onChunk?.({
-                type: ChunkType.IMAGE_COMPLETE,
-                image: {
-                  type: 'base64',
-                  images: images
-                }
-              })
-            }
-
-            // 转换消息并添加到结果
-            const convertedMessage = convertToMessage(toolResponse, toolCallResponse, model)
-            if (convertedMessage) {
-              confirmedTools.push(toolResponse)
-              toolResults.push(convertedMessage)
-            }
-          } catch (error) {
-            logger.error(`Error executing tool ${toolResponse.id}:`, error)
-            // 更新为错误状态
-            upsertMCPToolResponse(
-              allToolResponses,
-              {
-                ...toolResponse,
-                status: 'done',
-                response: {
-                  isError: true,
-                  content: [
-                    {
-                      type: 'text',
-                      text: `Error executing tool: ${error instanceof Error ? error.message : 'Unknown error'}`
-                    }
-                  ]
-                }
-              },
-              onChunk!
-            )
-          }
-        } else {
-          // 立即更新为cancelled状态
-          upsertMCPToolResponse(
-            allToolResponses,
-            {
-              ...toolResponse,
-              status: 'cancelled',
-              response: {
-                isError: false,
-                content: [
-                  {
-                    type: 'text',
-                    text: 'Tool call cancelled by user.'
-                  }
-                ]
-              }
-            },
-            onChunk!
-          )
-        }
-      })
-      .catch((error) => {
-        logger.error(`Error waiting for tool confirmation ${toolResponse.id}:`, error)
-        // 立即更新为cancelled状态
-        upsertMCPToolResponse(
-          allToolResponses,
-          {
-            ...toolResponse,
-            status: 'cancelled',
-            response: {
-              isError: true,
-              content: [
-                {
-                  type: 'text',
-                  text: `Error in confirmation process: ${error instanceof Error ? error.message : 'Unknown error'}`
-                }
-              ]
-            }
-          },
-          onChunk!
-        )
-      })
-
-    pendingPromises.push(processingPromise)
-  })
-
-  // 等待所有工具处理完成（但每个工具的状态已经实时更新）
-  await Promise.all(pendingPromises)
-
-  return { toolResults, confirmedToolResponses: confirmedTools }
-}
-
 export function mcpToolCallResponseToOpenAICompatibleMessage(
   mcpToolResponse: MCPToolResponse,
   resp: MCPCallToolResponse,