LLM-Driven Productivity Tracker with MCP Integration
An intelligent task management system powered by LangChain agents and Model Context Protocol (MCP), enabling natural language interaction for productivity tracking.
Features
- Multi-step AI Agent: Autonomous decision-making with LangChain for complex task workflows
- Natural Language Interface: Interact with your tasks using conversational AI
- MCP Server Integration: Exposes productivity tools via standardized protocol
- Claude Desktop Compatible: Works seamlessly as an MCP client
- Comprehensive Task Management:
- Log tasks with 8 categories (work, health, learning, personal, finance, social, hobby, self_care)
- 4 status types (todo, started, completed, blocked)
- Update and remove tasks by ID or name
- Time-based summaries (daily, weekly, monthly)
- Local LLM Support: Runs on Ollama with llama3.2 (no API costs)
Technologies
- Python 3.12
- LangChain (Agent framework)
- Ollama (llama3.2)
- Model Context Protocol (MCP)
- SQLite (Database)
- Claude Desktop API
- JSON-RPC
Prerequisites
- Python 3.12+
- Ollama installed with llama3.2 model
- Claude Desktop (optional, for MCP integration)
Installation
-
Clone the repository: git clone cd ProductivityTracker
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Install dependencies: pip install -r requirements.txt
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Install Ollama and pull llama3.2: ollama pull llama3.2
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Initialize the database: python -c "import database; database.init_db()"
Usage
Option 1: Local Agent (agent.py)
Run the agent locally with your own questions:
python agent.py
Modify the question variable in agent.py to test different queries.
Option 2: MCP Server with Claude Desktop
- Configure Claude Desktop by editing
%APPDATA%\Claude\claude_desktop_config.json:
{ "mcpServers": { "productivity-tracker": { "command": "C:\path\to\python.exe", "args": ["C:\path\to\ProductivityTracker\mcp_server.py"] } } }
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Restart Claude Desktop
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Interact naturally:
- "Log a task to review code for work as started"
- "Show me today's summary"
- "Update test task to completed"
Project Structure
ProductivityTracker/ ├── agent.py # Local LangChain agent with multi-step reasoning ├── mcp_server.py # MCP server exposing tools via protocol ├── tools.py # Tool definitions (log_task, get_summary, etc.) ├── database.py # SQLite operations with error handling ├── requirements.txt # Python dependencies └── productivity_tracker.db # SQLite database (auto-created)
Example Interactions
Log a task: "Log a morning workout for health category as completed"
Get summary: "How was my week?"
Update task: "Mark the code review task as completed"
Remove task: "Delete the duplicate dinner task"
Architecture
- Agent Pattern: AI decides which tools to use based on user intent
- Tool Pattern: 5 custom tools for task management operations
- MCP Integration: Standardized protocol for external agent communication
- Iterative Loop: Multi-step reasoning with context maintenance
Error Handling
- Database connection errors handled gracefully
- Invalid category/status inputs caught with helpful messages