Tesla Tessie MCP Server

A Model Context Protocol (MCP) server that provides Tesla vehicle telemetry data via the Tessie API. Exposes vehicle status, battery, charging, climate, and location data as tools for LLM consumption.

Features

• Intelligent Caching: Configurable data refresh intervals to minimize API calls
• Thread-Safe: Concurrent access to cached data is safely handled
• LLM-Optimized Output: Human-readable formatted strings for each data point
• Comprehensive Telemetry: 30+ tools covering all vehicle data categories

Installation

# Clone the repository
cd tessie_mcp

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Configuration

Environment Variables

Create a .env file in the project root:

# Required: Your Tessie API token
TESSIE_TOKEN=your_tessie_api_token_here

# Optional: Override vehicle plate (defaults to config.py)
VEHICLE_PLATE=34MIE386

# Optional: Data refresh interval in minutes (default: 5)
# Use 'realtime' to always fetch fresh data
TELEMETRY_INTERVAL=5

Vehicle Configuration

The vehicle plate can also be configured in config.py:

PLATE = '34MIE386'

Usage

1. Create .env File

Copy the example and add your Tessie API token:

cp .env.example .env

Edit .env:

TESSIE_TOKEN=your_tessie_api_token_here
VEHICLE_PLATE=34MIE386
TELEMETRY_INTERVAL=5

Get your token from: https://dash.tessie.com/settings/api

2. Running the MCP Server

Local Mode (STDIO)

source venv/bin/activate
python -m src.server

Remote Mode (HTTP/SSE)

source venv/bin/activate
python -m src.server --transport sse --port 8000

This starts an HTTP server with:

• SSE endpoint: http://your-server:8000/sse
• Health check: http://your-server:8000/health

3. Connecting to the MCP Server

Option A: Cursor IDE Integration

Add to your Cursor settings (~/.cursor/mcp.json or via Settings > MCP):

{
  "mcpServers": {
    "tessie": {
      "command": "/path/to/tessie_mcp/venv/bin/python",
      "args": ["-m", "src.server"],
      "cwd": "/path/to/tessie_mcp"
    }
  }
}

After adding, restart Cursor. The Tesla tools will appear in your tool list.

Option B: Claude Desktop Integration

Add to Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "tessie": {
      "command": "/path/to/tessie_mcp/venv/bin/python",
      "args": ["-m", "src.server"],
      "cwd": "/path/to/tessie_mcp"
    }
  }
}

Option C: Direct Testing with MCP Inspector

# Install MCP inspector
npx @anthropic-ai/mcp-inspector

# In another terminal, run your server
python -m src.server

Option D: Remote Connection (HTTP/SSE)

Start the server in SSE mode on your remote machine:

python -m src.server --transport sse --host 0.0.0.0 --port 8000

Then connect from a client using the SSE URL:

http://your-server-ip:8000/sse

For Cursor/Claude, configure with SSE transport:

{
  "mcpServers": {
    "tessie-remote": {
      "transport": "sse",
      "url": "http://your-server-ip:8000/sse"
    }
  }
}

Option E: Programmatic Usage (Python)

from src.telemetry import Telemetry

# Create telemetry instance (reads from .env automatically)
telemetry = Telemetry(plate="34MIE386", interval=5)

# Get formatted data (for LLMs)
print(telemetry.get_battery_level())      # "Battery is at 41%"
print(telemetry.get_charging_state())     # "Vehicle is not connected to a charger"
print(telemetry.get_location())           # "Vehicle is at 39.869430, 32.733333 facing N (2°)"

# Get raw data (for programmatic use)
print(telemetry._get_battery_level())     # 41
print(telemetry._get_energy_remaining())  # 27.76

Available Tools

Battery & Charging

Tool	Description
get_battery_level	Current battery percentage
get_energy_remaining	Remaining energy in kWh
get_lifetime_energy_used	Total lifetime energy consumption
get_battery_heater_on	Battery heater status (cold weather)
get_charging_state	Current charging status
get_charge_limit_soc	Charging limit percentage
get_charge_port_door_open	Charge port door status
get_minutes_to_full_charge	Time remaining to full charge
get_charging_complete_at	Estimated completion datetime
get_battery_summary	Comprehensive battery summary

Climate & Temperature

Tool	Description
get_is_climate_on	HVAC system status
get_outside_temp	Ambient temperature
get_allow_cabin_overheat_protection	COP enabled status
get_supports_fan_only_cabin_overheat_protection	Fan-only COP capability

Heaters

Tool	Description
get_seat_heater_left	Driver seat heater level
get_seat_heater_right	Passenger seat heater level
get_seat_heater_rear_left	Rear left seat heater level
get_seat_heater_rear_center	Rear center seat heater level
get_seat_heater_rear_right	Rear right seat heater level
get_steering_wheel_heater	Steering wheel heater status
get_side_mirror_heaters	Side mirror heaters status
get_wiper_blade_heater	Wiper blade heater status
get_all_heater_status	Summary of all heaters

Drive State & Location

Tool	Description
get_location	GPS coordinates and heading
get_speed	Current vehicle speed
get_power	Power usage/regeneration
get_shift_state	Current gear (P/R/N/D)
get_active_route	Active navigation info

Vehicle State

Tool	Description
get_in_service	Service mode status
get_sentry_mode	Sentry Mode status
get_display_name	Vehicle's custom name

Architecture

src/
├── __init__.py          # Package initialization
├── tessie_client.py     # Tessie API client
├── telemetry.py         # Telemetry class with caching
└── server.py            # MCP server entry point

Telemetry Class

The Telemetry class implements a dual-method pattern for each data field:

• Private methods (_get_*): Return raw values for programmatic use
• Public methods (get_*): Return formatted strings for LLM consumption

Example:

telemetry._get_battery_level()  # Returns: 41
telemetry.get_battery_level()   # Returns: "Battery is at 41%"

Caching Strategy

• Data is cached with timestamps
• On each request, elapsed time is checked against the interval
• If stale, fresh data is fetched from Tessie API
• Thread-safe access using locks

License

MIT