📋 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.

✨ 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

Related MCP server: Changerawr MCP Server

🚀 Installation

npm install

🎮 Usage

Start the MCP server:

npm start

Or run in development mode with auto-restart:

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:

# 📝 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:

{
  "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:

{
  "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:

log_activity({
  tool_name: "Warp",
  log_message: "Created React component for user dashboard"
})

With comprehensive metrics:

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:

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:

{
  "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:

# 📝 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.