The TeslaMate MCP server enables AI assistants to query and analyze Tesla vehicle data from a TeslaMate PostgreSQL database through natural language.

Key Capabilities:

Vehicle Information: Retrieve basic details (VIN, model, color), current status (location, battery level, temperature), and software update history
Battery Analytics: Monitor health and degradation over time, track daily usage patterns
Driving Insights: Access monthly/daily summaries, identify longest drives, view total distance and efficiency metrics
Efficiency Analysis: Analyze trends by month and temperature, detect unusual power consumption
Charging Data: View all charging sessions and statistics by location
Location Tracking: Identify most visited places and analyze location patterns
Tire Monitoring: Track tire pressure trends over time
Custom Queries: Execute safe, read-only SQL SELECT queries with complete database schema access and built-in validation to prevent data modification

All data access is secure and read-only, making it safe for comprehensive Tesla vehicle data exploration and analysis.

TeslaMate MCP Server

Trust Score

A Model Context Protocol (MCP) server that connects your TeslaMate database to AI assistants, enabling natural language queries about your Tesla data.

Features

🚗 18 Predefined Queries - Battery health, efficiency, charging, driving patterns, and more
🔍 Custom SQL Support - Execute safe SELECT queries with built-in validation
🗄️ Database Schema Access - Explore your TeslaMate database structure
🔒 Optional Authentication - Bearer token support for remote deployments
🏗️ Modular Architecture - Clean, maintainable codebase

Related MCP server: Tesla MCP Server

Prerequisites

TeslaMate running with PostgreSQL
Python 3.11+ (for local) or Docker (for remote)

Quick Start

Local Setup (Cursor/Claude Desktop)

git clone https://github.com/cobanov/teslamate-mcp.git cd teslamate-mcp cp env.example .env # Edit .env with your DATABASE_URL uv sync

Configure your MCP client:

{ "mcpServers": { "teslamate": { "command": "uv", "args": ["--directory", "/path/to/teslamate-mcp", "run", "main.py"] } } }

Remote Setup (Docker)

git clone https://github.com/cobanov/teslamate-mcp.git cd teslamate-mcp cp env.example .env # Edit .env with your DATABASE_URL docker-compose up -d

Server available at: http://localhost:8888/mcp

Configuration

Create .env file:

DATABASE_URL=postgresql://user:pass@host:5432/teslamate AUTH_TOKEN= # Optional: for remote auth

Generate auth token (optional):

python utils/generate_token.py

Available Tools

Predefined Queries (18 tools)

Vehicle Info:

get_basic_car_information - VIN, model, firmware
get_current_car_status - Real-time status, location, battery
get_software_update_history - Firmware update timeline

Battery & Health:

get_battery_health_summary - Current health metrics
get_battery_degradation_over_time - Historical capacity
get_daily_battery_usage_patterns - Usage patterns
get_tire_pressure_weekly_trends - Tire pressure tracking

Driving Analytics:

get_monthly_driving_summary - Monthly statistics
get_daily_driving_patterns - Driving habits
get_longest_drives_by_distance - Top trips
get_total_distance_and_efficiency - Lifetime stats
get_drive_summary_per_day - Daily summaries

Efficiency:

get_efficiency_by_month_and_temperature - Seasonal analysis
get_average_efficiency_by_temperature - Temperature impact
get_unusual_power_consumption - Anomaly detection

Charging & Location:

get_charging_by_location - Charging patterns
get_all_charging_sessions_summary - Complete history
get_most_visited_locations - Frequent places

Custom Queries (2 tools)

get_database_schema - View database structure
run_sql - Execute custom SELECT queries (read-only, validated)

Example Queries

"What's my current battery health?" "Show me my longest drives" "How does cold weather affect my efficiency?" "Where do I charge most often?" "Run a SQL query to find drives over 100km"

Project Structure

teslamate-mcp/ ├── src/ # Core modules │ ├── config.py # Configuration │ ├── database.py # DB operations │ ├── tools.py # Tool registry │ └── validators.py # SQL validation ├── queries/ # 18 SQL query files ├── data/ # Database schema ├── utils/ # Helper scripts ├── main.py # Local (STDIO) ├── main_remote.py # Remote (HTTP) ├── Dockerfile └── docker-compose.yml

Development

Adding New Queries

Create SQL file in queries/:

-- queries/my_query.sql SELECT * FROM my_table;

Add to src/tools.py:

ToolDefinition( name="get_my_data", description="What this returns", sql_file="my_query.sql", )

Restart server - tool auto-registers!

Testing

python test_server.py

Security

Authentication: Optional bearer token for remote access
SQL Validation: Only SELECT queries allowed
Read-only: No data modification possible
Use HTTPS: In production environments

License

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

Acknowledgments

TeslaMate - Tesla data logging
Model Context Protocol - AI integration protocol

teslamate-mcp