Frigate MCP Server

Frigate MCP Server

A Model Context Protocol server that enables AI assistants to control and query Frigate NVR via natural language, providing 59 tools for system management, events, cameras, recordings, review, exports, labels, and classification.

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Frigate MCP Server

A Model Context Protocol (MCP) server for Frigate NVR, built with FastMCP.

Control and query your Frigate NVR instance through AI assistants like Claude Desktop, Claude Code, or any MCP-compatible client using natural language.

Features

59 tools across 8 categories, mapped 1:1 to Frigate's v0.17.x HTTP API:

Category Tools Description
System 9 Version, stats (+history), config (get/save/schema), logs, restart, Frigate+ models
Events 16 List, explore, by-id, search, summary, create/end/delete, retain, false-positive, sub-label, recognized plate, attributes, description, regenerate description
Cameras 2 Latest frame, latest "best" thumbnail per camera + label
Recordings 4 Summary, storage, list segments, recording gaps
Review 11 List, by-id, by-event, by-ids, summary, mark/unmark viewed, delete, motion activity, AI summary
Exports 5 List, get, create, delete, rename
Labels 4 Labels, sub-labels, timeline, hourly timeline
Classification 8 Faces CRUD (folder/delete/rename/reprocess/list), recognized plates, LPR reprocess, event thumbnail/snapshot

PTZ camera control is not included — Frigate exposes PTZ over MQTT, not HTTP.

Quick Start

Prerequisites

  • Python 3.11+
  • A running Frigate instance

Install

# Clone the repo
git clone https://github.com/jakekeeys/frigate-mcp.git
cd frigate-mcp

# Install with pip
pip install -e .

# Or with uv
uv pip install -e .

Configure

Set the FRIGATE_URL environment variable pointing to your Frigate instance:

export FRIGATE_URL=http://192.168.1.50:5000

Or create a .env file (see .env.example):

FRIGATE_URL=http://192.168.1.50:5000

Run

# Run via module
python -m frigate_mcp

# Or via the installed entry point
frigate-mcp

MCP Client Configuration

Claude Desktop

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS, %APPDATA%\Claude\claude_desktop_config.json on Windows):

{
  "mcpServers": {
    "frigate": {
      "command": "python",
      "args": ["-m", "frigate_mcp"],
      "env": {
        "FRIGATE_URL": "http://your-frigate-ip:5000"
      }
    }
  }
}

Claude Code

claude mcp add frigate -- python -m frigate_mcp

Then set FRIGATE_URL in your environment or .env file.

Using uvx (no install needed)

{
  "mcpServers": {
    "frigate": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/jakekeeys/frigate-mcp", "frigate-mcp"],
      "env": {
        "FRIGATE_URL": "http://your-frigate-ip:5000"
      }
    }
  }
}

Example Queries

Once connected, you can ask your AI assistant things like:

  • "What cameras are configured in Frigate?"
  • "Show me recent person detections"
  • "Were there any cars in the driveway today?"
  • "Search for delivery person events"
  • "Show me the latest frame from the front door camera"
  • "How much recording storage is being used?"
  • "Mark all review items from today as reviewed"
  • "Create an export of the backyard camera from 2pm to 3pm"
  • "What faces has Frigate learned?"
  • "Summarise everything that happened in the review queue overnight"
  • "Show me the system stats"

Configuration

Environment Variable Default Description
FRIGATE_URL http://localhost:5000 Frigate instance URL
FRIGATE_TIMEOUT 30 HTTP request timeout (seconds)

Architecture

src/frigate_mcp/
    __init__.py          # Package version
    __main__.py          # CLI entry point (stdio transport)
    config.py            # Pydantic Settings from env vars
    server.py            # FrigateMCPServer (FastMCP wrapper)
    client/
        rest_client.py   # Async httpx client for Frigate API
    tools/
        tools_system.py         # System/config tools
        tools_events.py         # Event CRUD, search, attributes, description
        tools_cameras.py        # Camera frames + best-per-label thumbnails
        tools_recordings.py     # Recording summary, segments, gaps
        tools_review.py         # Review queue + GenAI summary
        tools_exports.py        # Video exports
        tools_labels.py         # Labels, sub-labels, timeline
        tools_classification.py # Faces + recognised plates + event media

License

MIT

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