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Frigate MCP Server

by jakekeeys
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Python
Remote

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.

Related MCP server: YARR Media Stack MCP Server

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

This server cannot be installed

A
license - permissive license
-
quality - not tested
C
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

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