# 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
1. Clone the repository:
git clone cd ProductivityTracker
2. Install dependencies:
pip install -r requirements.txt
3. Install Ollama and pull llama3.2:
ollama pull llama3.2
4. 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
1. 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"] } } }
2. Restart Claude Desktop
3. 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
