MCP Work History Server

# 📋 MCP Work History Server 🤖 A Model Context Protocol (MCP) server that allows AI tools to log their activities to daily worklog files with detailed tracking of tool names, AI models, and timestamps. <a href="https://glama.ai/mcp/servers/@nocoo/mcp-work-history"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@nocoo/mcp-work-history/badge" alt="Work History Server MCP server" /> </a> ## ✨ Features - 🕐 **Precise timestamps** - Logs activities with HH:MM format - 🔧 **Tool tracking** - Records which AI tool performed the action - 🧠 **Model tracking** - Tracks which AI model was used (e.g., gemini-2.5-pro, claude-3-sonnet) - 📊 **Comprehensive metrics** - Token usage, context length, duration, cost tracking - 🏷️ **Tagging system** - Categorize activities with custom tags - ✅❌ **Success/failure tracking** - Log both successful operations and errors - 📁 **Daily organization** - Creates separate markdown files for each day - 📝 **Clean format** - Bullet-point style entries for easy scanning - 🎯 **MCP compatible** - Works with any MCP-enabled AI client ## 🚀 Installation ```bash npm install ``` ## 🎮 Usage Start the MCP server: ```bash npm start ``` Or run in development mode with auto-restart: ```bash npm run dev ``` ## 🛠️ MCP Tool The server provides one tool: ### `log_activity` Logs an AI tool's activity to the current day's worklog file in a concise, scannable format. **Parameters:** **Required:** - `tool_name` (string): Name of the AI tool (e.g., "Warp", "Claude Code", "GitHub Copilot") - `log_message` (string): Detailed description of what was accomplished **Optional:** - `ai_model` (string): AI model used (e.g., "gemini-2.5-pro", "claude-3-sonnet", "gpt-4") - `tokens_used` (number): Total tokens consumed in the request - `input_tokens` (number): Input tokens used (alternative to tokens_used) - `output_tokens` (number): Output tokens generated (alternative to tokens_used) - `context_length` (number): Context window length used (in thousands) - `duration_ms` (number): Duration of the operation in milliseconds - `cost_usd` (number): Estimated cost in USD - `success` (boolean): Whether the operation was successful (defaults to true) - `error_message` (string): Error message if operation failed - `tags` (array): Tags to categorize the activity (e.g., ["coding", "debugging", "refactoring"]) **Example log entries:** ```markdown # 📝 Work Log - 2024-01-15 - ✅ 08:31 - Warp (gemini-2.5-pro): Refactored authentication module to use JWT tokens (1250 tokens | 8k ctx | 2.3s | $0.0043 | [refactoring, auth]) - ✅ 09:15 - Claude Code (claude-3-sonnet): Fixed database connection pooling issue (850→320 tokens | 1.1s | $0.0021) - ❌ 10:42 - GitHub Copilot (gpt-4): Attempted to implement user profile endpoint (❌ Timeout error | [coding, api]) - ✅ 11:30 - Warp: Quick code review and suggestions (500 tokens | 0.8s) ``` ## 📂 Log File Structure Logs are stored in the `logs/` directory with the naming pattern `worklog-YYYY-MM-DD.md`. Each log file contains: - 📝 Emoji-enhanced date header - 🕐 Timestamped bullet-point entries - 🔧 Tool name and AI model information - 📋 Concise activity descriptions ## ⚙️ MCP Configuration ### For Warp AI Add this server to your Warp MCP configuration: ```json { "mcp-work-history": { "command": "node", "args": ["/Users/your-username/path/to/mcp-work-history/src/index.js"], "env": {}, "working_directory": null, "start_on_launch": true } } ``` ### For Claude Desktop Add to your `claude_desktop_config.json`: ```json { "mcpServers": { "work-history": { "command": "node", "args": ["/absolute/path/to/mcp-work-history/src/index.js"] } } } ``` ### Example Usage in AI Tools Once configured, AI tools can log their activities like this: **Basic usage:** ```javascript log_activity({ tool_name: "Warp", log_message: "Created React component for user dashboard" }) ``` **With comprehensive metrics:** ```javascript log_activity({ tool_name: "Warp", ai_model: "gemini-2.5-pro", log_message: "Refactored authentication system with OAuth integration", tokens_used: 1250, context_length: 8, duration_ms: 2300, cost_usd: 0.0043, success: true, tags: ["refactoring", "auth", "oauth"] }) ``` **Error logging:** ```javascript log_activity({ tool_name: "GitHub Copilot", ai_model: "gpt-4", log_message: "Attempted to implement user profile endpoint", input_tokens: 800, output_tokens: 0, success: false, error_message: "Timeout error", tags: ["coding", "api"] }) ``` ## 🗂️ Project Structure ``` mcp-work-history/ ├── 📄 src/index.js # Main MCP server code ├── 📁 logs/ # Daily worklog files (auto-created) │ ├── worklog-2024-01-15.md │ └── worklog-2024-01-16.md ├── 📦 package.json # Dependencies and scripts ├── 🚫 .gitignore # Git ignore rules └── 📋 README.md # This file ``` ## 🤝 Contributing 1. Fork the repository 2. Create your feature branch (`git checkout -b feature/amazing-feature`) 3. Commit your changes (`git commit -m 'Add some amazing feature'`) 4. Push to the branch (`git push origin feature/amazing-feature`) 5. Open a Pull Request ## 🎯 Real-World Example: Warp AI Integration Here's how to set up automatic activity logging in Warp AI: ### Step 1: Configure MCP Server in Warp Add the following to your Warp MCP configuration: ```json { "mcp-work-history": { "command": "node", "args": ["/Users/nocoo/Workspace/mcp-work-history/src/index.js"], "env": {}, "working_directory": null, "start_on_launch": true } } ``` ### Step 2: Add Logging Rule to Warp Configure Warp with this rule to automatically log AI activities: > **Rule:** "When AI task is done, use mcp-work-history to log this time AI task details. Send AI tool name (Warp), model used, detailed time, and a brief summary of this time task and result." ### Step 3: See It in Action  *Screenshot showing the MCP Work History server automatically logging AI activities in Warp* ### What Gets Logged With this setup, every AI interaction in Warp will automatically create entries like: ```markdown # 📝 Work Log - 2024-12-06 - ✅ 14:32 - Warp (gemini-2.5-pro): Refactored React component to use custom hooks for state management (1240 tokens | 4.2s | [refactoring, react]) - ✅ 14:45 - Warp (gemini-2.5-pro): Fixed TypeScript type errors in authentication module (890 tokens | 2.1s | [bugfix, typescript]) - ✅ 15:10 - Warp (gemini-2.5-pro): Added comprehensive unit tests for user service (1560 tokens | 3.8s | [testing, unit-tests]) ``` ### Benefits - 📊 **Automatic tracking** - No manual logging required - 🔍 **Detailed insights** - Track token usage, performance, and costs - 📈 **Progress monitoring** - See your daily coding accomplishments - 🏷️ **Activity categorization** - Organize work with tags - 💰 **Cost tracking** - Monitor AI usage costs over time ## 📄 License MIT License - see the LICENSE file for details.