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

| Tool | Description | |---|---| | get_snapshot | Fetch a live image from a camera — saves to disk, returns file path | | list_cameras | Browse the registry with filters (city, location, category) | | search_cameras | Search by name, location, or category | | add_local_camera | Add a camera to your local collection | | list_local | Show your locally-added cameras | | remove_local | Delete a locally-added camera | | submit_local | Share local cameras upstream via GitHub issue | | report_camera | Report a broken or low-quality camera | | check_config | Show API key configuration status |

Upstream vs local cameras

The registry has two layers:

• Upstream — the global registry fetched from GitHub on every server start. These are the ~29,000 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

~31,000 cameras across eleven countries:

| Country | Count | Sources | |---|---|---| | US | 26,142 | NYC DOT, NY 511, WSDOT, Caltrans CWWP2, CDOT CoTrip, VDOT 511, FDOT FL511, NCDOT, PennDOT 511PA, Arizona ADOT, Oregon ODOT, Nevada NDOT, Utah UDOT, Wisconsin WisDOT, New England 511, Louisiana LADOTD, Alaska DOT&PF | | FI | 2,223 | Digitraffic weather cameras (Fintraffic) | | CA | 1,292 | Ontario MTO, Alberta 511 | | HK | 995 | Hong Kong Transport Department | | GB | 424 | London TfL JamCams | | NZ | 251 | NZTA nationwide highways | | BR | 195 | CET São Paulo urban traffic | | AU | 159 | Sydney + Regional NSW | | JP | 98 | NEXCO East expressways | | SG | 90 | Singapore LTA | | IE | 53 | TII motorway cams (M50 Dublin) |

Every camera has country, city, location, timezone, and coordinates (lat/lng).

Self-healing

A GitHub Action runs nightly at 3 AM UTC:

• Checks cameras not validated in the last 7 days, plus any flagged as suspect
• First failure marks as suspect, second consecutive failure removes and opens a GitHub issue
• Vision AI (GPT-4o-mini via GitHub Models) catches cameras returning error pages
• Suspect cameras that recover are cleared automatically

Security

• SSRF protection — blocks private IPs, cloud metadata endpoints, non-HTTP protocols, and DNS rebinding
• Content-type whitelist — only image/jpeg and image/png accepted
• Magic byte detection — validates JPEG/PNG by file header when CDN returns wrong content-type
• Push/PR cap — max 500 cameras per push to prevent DoS via oversized PRs
• Random filenames — snapshots use random hex filenames, no camera ID in the path

API Keys (optional)

Most cameras work out of the box. Some require a free API key. If a snapshot fails with a key error, the response tells you where to sign up and how to configure it.

Create ~/.openeagleeye/config.json:

{
  "api_keys": {
    "PROVIDER_API_KEY": "your-key-here"
  }
}

Use check_config to see which cameras need keys and whether yours are set.

Adding cameras

1. Find a direct-image URL (must return image/jpeg or image/png on plain GET)
2. add_local_camera with the URL, city, location, timezone, and optional coordinates
3. get_snapshot to test it
4. submit_local to share upstream — requires the gh CLI (gh auth login)

Local cameras work immediately and don't need upstream approval to be useful.

Good sources: city DOTs, weather stations, ski resorts, national parks, ports, airports.

File layout

All runtime data lives in ~/.openeagleeye/:

~/.openeagleeye/
  cameras.json          # Upstream registry (fetched from GitHub on boot)
  local-cameras.json    # Your locally-added cameras
  .registry-state.json  # Validation state (active/suspect/offline)
  snapshots/            # Downloaded camera images
  config.json           # API keys

Contributing

Pull requests welcome. See CONTRIBUTING.md for guidelines.

License

MIT