teslamate-mcp

TeslaMate MCP Server

A Model Context Protocol (MCP) server that provides access to your TeslaMate database, allowing AI assistants to query Tesla vehicle data and analytics.

Overview

This MCP server connects to your TeslaMate PostgreSQL database and exposes various tools to retrieve Tesla vehicle information, driving statistics, charging data, battery health, efficiency metrics, and location analytics. It's designed to work with MCP-compatible AI assistants like Claude Desktop, enabling natural language queries about your Tesla data.

Prerequisites

• TeslaMate running with a PostgreSQL database
• Python 3.11 or higher
• Access to your TeslaMate database

Installation

1. Clone this repository:
   git clone https://github.com/yourusername/teslamate-mcp.git cd teslamate-mcp
2. Install dependencies using uv (recommended):
   uv sync
   Or using pip:
   pip install -r requirements.txt
3. Create a .env file in the project root:
   DATABASE_URL=postgresql://username:password@hostname:port/teslamate

Configuration

Environment Variables

• DATABASE_URL: PostgreSQL connection string for your TeslaMate database

MCP Client Configuration

To use this server with Claude Desktop, add the following to your MCP configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

Usage

Running the Server

Example Queries

Once configured with an MCP client, you can ask natural language questions organized by category:

Basic Vehicle Information

• "What's my Tesla's basic information?"
• "Show me my current car status"
• "What software updates has my Tesla received?"

Battery and Health

• "How is my battery health?"
• "Show me battery degradation over time"
• "What are my daily battery usage patterns?"
• "How are my tire pressures trending?"

Driving Analytics

• "Show me my monthly driving summary"
• "What are my daily driving patterns?"
• "What are my longest drives by distance?"
• "What's my total distance driven and efficiency?"

Efficiency Analysis

• "How does temperature affect my efficiency?"
• "Show me efficiency trends by month and temperature"
• "Are there any unusual power consumption patterns?"

Charging and Location Data

• "Where do I charge most frequently?"
• "Show me all my charging sessions summary"
• "What are my most visited locations?"

Adding New Queries

1. Create a new SQL file in the queries/ directory
2. Add a corresponding tool function in main.py
3. Follow the existing pattern for error handling and database connections

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• TeslaMate - Tesla data logging software
• Model Context Protocol - Protocol for AI-tool integration

For bugs and feature requests, please open an issue on GitHub.