Open Eagle Eye

MCP server that gives AI agents instant access to public camera feeds worldwide. One HTTP GET, sub-second captures, no browser automation, no stream decoding.

Why

Most camera APIs require authentication, serve video streams, or hide images behind JavaScript rendering. Open Eagle Eye only indexes cameras that return a JPEG or PNG on a plain HTTP GET — the simplest possible integration. Agents don't need to render pages or decode video. They just fetch an image.

The registry is self-healing. A GitHub Action runs nightly, checks every camera, retries failures before removing them, and uses vision AI to catch cameras that return error pages instead of live feeds. Dead cameras get flagged automatically.

Quick start

{
  "mcpServers": {
    "openeagleeye": {
      "command": "npx",
      "args": ["-y", "openeagleeye"]
    }
  }
}

Or install globally:

npm install -g openeagleeye
openeagleeye

On first run, the server fetches the latest camera registry from GitHub and caches it locally in ~/.openeagleeye/. Subsequent starts refresh the cache automatically.

How it works

A valid camera URL is any endpoint that returns a JPEG or PNG on a plain HTTP GET. Most city traffic cameras, weather stations, and park cams expose exactly this. The server fetches the image, saves it to disk, and returns the file path.

MCP Tools

Upstream vs local cameras

The registry has two layers:

• Upstream — the global registry fetched from GitHub on every server start. These are the ~32,000 validated public cameras.
• Local — cameras you add yourself via add_local_camera. They persist in ~/.openeagleeye/local-cameras.json, survive restarts, and appear in list_cameras/search_cameras with source: "local". Share them upstream anytime with submit_local.

Filtering

Every camera has a city field. Use list_cameras with city: "Sydney" to get a short, focused list instead of dumping all cameras into context.

Output format

Every tool returns structured JSON. Snapshots save to disk and return the file path — the MCP server runs as a local subprocess, so the agent has filesystem access.

Snapshot response:

{
  "success": true,
  "file_path": "/home/user/.openeagleeye/snapshots/a1b2c3d4e5f6a7b8.jpg",
  "size_bytes": 14579,
  "content_type": "image/jpeg",
  "camera": {
    "id": "nyc-bb-21-north-rdwy-at-above-south-st",
    "name": "BB-21 North Rdwy @ Above South St",
    "city": "New York",
    "location": "Manhattan, New York, USA",
    "coordinates": { "lat": 40.708, "lng": -73.999 }
  }
}

Registry

~32,000 cameras across eleven countries (32,096 verified):

... View full README on GitHub