MCP Work History Server

Instructions

Implementation Reference

• src/index.js:111-237 (handler)

  The handler function that executes the log_activity tool. It parses arguments, constructs a formatted log entry with optional metadata (tokens, duration, cost, etc.), appends it to a daily Markdown worklog file in the 'logs' directory, and returns a success or error message.
  async handleLogActivity(args) { try { const { tool_name, log_message, ai_model, tokens_used, input_tokens, output_tokens, context_length, duration_ms, cost_usd, success = true, error_message, tags } = args; if (!tool_name || !log_message) { throw new Error("tool_name and log_message are required"); } const now = new Date(); const dateStr = now.toISOString().split('T')[0]; // YYYY-MM-DD format const timeStr = now.toLocaleTimeString('en-US', { hour12: false, hour: '2-digit', minute: '2-digit' }); const logsDir = path.join(__dirname, '..', 'logs'); const logFileName = `worklog-${dateStr}.md`; const logFilePath = path.join(logsDir, logFileName); // Ensure logs directory exists await fs.mkdir(logsDir, { recursive: true }); let logContent = ''; let fileExists = false; try { await fs.access(logFilePath); fileExists = true; logContent = await fs.readFile(logFilePath, 'utf-8'); } catch (error) { // File doesn't exist, create new content logContent = `# 📝 Work Log - ${dateStr}\n\n`; } // Build the log entry with optional metadata let logEntry = `${timeStr} - ${tool_name}`; if (ai_model) { logEntry += ` (${ai_model})`; } logEntry += `: ${log_message}`; // Add optional metadata const metadata = []; if (tokens_used !== undefined) { metadata.push(`${tokens_used} tokens`); } else if (input_tokens !== undefined || output_tokens !== undefined) { const inTokens = input_tokens || 0; const outTokens = output_tokens || 0; metadata.push(`${inTokens + outTokens} tokens (${inTokens}→${outTokens})`); } if (context_length !== undefined) { metadata.push(`${context_length}k ctx`); } if (duration_ms !== undefined) { const seconds = duration_ms >= 1000 ? `${(duration_ms / 1000).toFixed(1)}s` : `${duration_ms}ms`; metadata.push(seconds); } if (cost_usd !== undefined) { metadata.push(`$${cost_usd.toFixed(4)}`); } if (!success && error_message) { metadata.push(`❌ ${error_message}`); } if (tags && tags.length > 0) { metadata.push(`[${tags.join(', ')}]`); } if (metadata.length > 0) { logEntry += ` (${metadata.join(' | ')})`; } const statusIcon = success ? '✅' : '❌'; const newEntry = `- ${statusIcon} ${logEntry}\n`; // If it's a new file, add it directly. Otherwise, append to existing content if (!fileExists) { logContent += newEntry; } else { // Insert at the end of the file logContent += newEntry; } await fs.writeFile(logFilePath, logContent, 'utf-8'); return { content: [ { type: "text", text: `✅ Activity logged: ${logEntry}${fileExists ? '' : ' (new file created)'}` } ] }; } catch (error) { return { content: [ { type: "text", text: `❌ Error logging activity: ${error.message}` } ], isError: true }; } }
• src/index.js:40-96 (schema)

  Input schema for the log_activity tool, defining required (tool_name, log_message) and optional parameters for logging AI activity metrics.
  inputSchema: { type: "object", properties: { tool_name: { type: "string", description: "Name of the AI tool that performed the activity (e.g., 'Warp', 'Claude Code', 'GitHub Copilot')" }, log_message: { type: "string", description: "Detailed log message describing what was accomplished" }, ai_model: { type: "string", description: "AI model used (e.g., 'gemini-2.5-pro', 'claude-3-sonnet', 'gpt-4')" }, tokens_used: { type: "number", description: "Total tokens consumed in the request (optional)" }, input_tokens: { type: "number", description: "Input tokens used (optional)" }, output_tokens: { type: "number", description: "Output tokens generated (optional)" }, context_length: { type: "number", description: "Context window length used (optional)" }, duration_ms: { type: "number", description: "Duration of the operation in milliseconds (optional)" }, cost_usd: { type: "number", description: "Estimated cost in USD (optional)" }, success: { type: "boolean", description: "Whether the operation was successful (optional, defaults to true)" }, error_message: { type: "string", description: "Error message if operation failed (optional)" }, tags: { type: "array", items: { type: "string" }, description: "Tags to categorize the activity (e.g., ['coding', 'debugging', 'refactoring']) (optional)" } }, required: ["tool_name", "log_message"] }
• src/index.js:102-108 (registration)

  Registration of the CallToolRequest handler that routes 'log_activity' calls to the handleLogActivity method.
  this.server.setRequestHandler(CallToolRequestSchema, async (request) => { if (request.params.name === "log_activity") { return await this.handleLogActivity(request.params.arguments); } throw new Error(`Unknown tool: ${request.params.name}`); });
• src/index.js:35-99 (registration)

  Tool registration in the ListToolsRequest response, including name, description, and inputSchema for log_activity.
  return { tools: [ { name: "log_activity", description: "Log AI tool activity to a daily worklog file with comprehensive metrics", inputSchema: { type: "object", properties: { tool_name: { type: "string", description: "Name of the AI tool that performed the activity (e.g., 'Warp', 'Claude Code', 'GitHub Copilot')" }, log_message: { type: "string", description: "Detailed log message describing what was accomplished" }, ai_model: { type: "string", description: "AI model used (e.g., 'gemini-2.5-pro', 'claude-3-sonnet', 'gpt-4')" }, tokens_used: { type: "number", description: "Total tokens consumed in the request (optional)" }, input_tokens: { type: "number", description: "Input tokens used (optional)" }, output_tokens: { type: "number", description: "Output tokens generated (optional)" }, context_length: { type: "number", description: "Context window length used (optional)" }, duration_ms: { type: "number", description: "Duration of the operation in milliseconds (optional)" }, cost_usd: { type: "number", description: "Estimated cost in USD (optional)" }, success: { type: "boolean", description: "Whether the operation was successful (optional, defaults to true)" }, error_message: { type: "string", description: "Error message if operation failed (optional)" }, tags: { type: "array", items: { type: "string" }, description: "Tags to categorize the activity (e.g., ['coding', 'debugging', 'refactoring']) (optional)" } }, required: ["tool_name", "log_message"] } } ] };