taskqueue-mcp

Instructions

Implementation Reference

• src/server/tools.ts:196-225 (schema)

  Tool definition and input schema for 'generate_project_plan', specifying parameters like prompt, provider, model, and optional attachments.
  const generateProjectPlanTool: Tool = { name: "generate_project_plan", description: "Use an LLM to generate a project plan and tasks from a prompt. The LLM will analyze the prompt and any attached files to create a structured project plan.", inputSchema: { type: "object", properties: { prompt: { type: "string", description: "The prompt text or file path to use for generating the project plan.", }, provider: { type: "string", enum: ["openai", "google", "deepseek"], description: "The LLM provider to use (requires corresponding API key to be set).", }, model: { type: "string", description: "The specific model to use (e.g., 'gpt-4-turbo' for OpenAI).", }, attachments: { type: "array", items: { type: "string", }, description: "Optional array of paths to files to attach as context. There is no need to read the files before calling this tool!", }, }, required: ["prompt", "provider", "model"], }, };
• src/server/toolExecutors.ts:185-224 (handler)

  MCP tool executor for 'generate_project_plan': performs input validation on arguments and delegates execution to TaskManager.generateProjectPlan method.
  const generateProjectPlanToolExecutor: ToolExecutor = { name: "generate_project_plan", async execute(taskManager, args) { // 1. Argument Validation const prompt = validateRequiredStringParam(args.prompt, "prompt"); const provider = validateRequiredStringParam(args.provider, "provider"); const model = validateRequiredStringParam(args.model, "model"); // Validate optional attachments let attachments: string[] = []; if (args.attachments !== undefined) { if (!Array.isArray(args.attachments)) { throw new AppError( "Invalid attachments: must be an array of strings", AppErrorCode.InvalidArgument ); } attachments = args.attachments.map((att, index) => { if (typeof att !== "string") { throw new AppError( `Invalid attachment at index ${index}: must be a string`, AppErrorCode.InvalidArgument ); } return att; }); } // 2. Core Logic Execution const resultData = await taskManager.generateProjectPlan({ prompt, provider, model, attachments, }); // 3. Return raw success data return resultData; }, };
• src/server/toolExecutors.ts:225-225 (registration)

  Registers the generateProjectPlanToolExecutor in the toolExecutorMap used by executeToolAndHandleErrors to dispatch tool calls.
  toolExecutorMap.set(generateProjectPlanToolExecutor.name, generateProjectPlanToolExecutor);
• src/server/tools.ts:439-439 (registration)

  Includes generateProjectPlanTool in the ALL_TOOLS export array for MCP tool listing.
  generateProjectPlanTool,
• src/server/TaskManager.ts:156-263 (handler)

  Core implementation of the tool logic: reads attachments, builds LLM prompt with schema, dynamically imports AI SDK provider based on input, generates structured project plan and tasks via generateObject, creates project, and handles provider-specific errors like missing API keys or invalid models.
  public async generateProjectPlan({ prompt, provider, model, attachments, }: { prompt: string; provider: string; model: string; attachments: string[]; }): Promise<ProjectCreationSuccessData> { await this.ensureInitialized(); // Read all attachment files const attachmentContents: string[] = []; for (const filename of attachments) { try { const content = await this.fileSystemService.readAttachmentFile(filename); attachmentContents.push(content); } catch (error) { throw new AppError(`Failed to read attachment file: ${filename}`, AppErrorCode.FileReadError, error); } } // Define the schema for the LLM's response using jsonSchema helper const projectPlanSchema = jsonSchema<ProjectPlanOutput>({ type: "object", properties: { projectPlan: { type: "string" }, tasks: { type: "array", items: { type: "object", properties: { title: { type: "string" }, description: { type: "string" }, toolRecommendations: { type: "string" }, ruleRecommendations: { type: "string" }, }, required: ["title", "description"], }, }, }, required: ["tasks"], }); // Wrap prompt and attachments in XML tags let llmPrompt = `<prompt>${prompt}</prompt>`; llmPrompt += `\n<outputFormat>Return your output as JSON formatted according to the following schema: ${JSON.stringify(projectPlanSchema, null, 2)}</outputFormat>` for (const content of attachmentContents) { llmPrompt += `\n<attachment>${content}</attachment>`; } // Import and configure the appropriate provider let modelProvider; switch (provider) { case "openai": const { openai } = await import("@ai-sdk/openai"); modelProvider = openai(model); break; case "google": const { google } = await import("@ai-sdk/google"); modelProvider = google(model); break; case "deepseek": const { deepseek } = await import("@ai-sdk/deepseek"); modelProvider = deepseek(model); break; default: throw new AppError(`Invalid provider: ${provider}`, AppErrorCode.InvalidProvider); } try { const { object } = await generateObject({ model: modelProvider, schema: projectPlanSchema, prompt: llmPrompt, }); return await this.createProject(prompt, object.tasks, object.projectPlan); } catch (err: any) { if (err.name === 'LoadAPIKeyError' || err.message.includes('API key is missing') || err.message.includes('You didn\'t provide an API key') || err.message.includes('unregistered callers') || (err.responseBody && err.responseBody.includes('Authentication Fails'))) { throw new AppError( `Missing API key environment variable required for ${provider}`, AppErrorCode.ConfigurationError, err ); } // Check for invalid model errors by looking at the error code, type, and message if ((err.data?.error?.code === 'model_not_found') && err.message.includes('model')) { throw new AppError( `Invalid model: ${model} is not available for ${provider}`, AppErrorCode.InvalidModel, err ); } // For unknown errors, preserve the original error but wrap it throw new AppError( "Failed to generate project plan due to an unexpected error", AppErrorCode.LLMGenerationError, err ); } }