Vancam

Vancam MCP Server

Vancam.ai — Traffic Cameras & Road Conditions — as an MCP (Model Context Protocol) server. Gives AI agents live access to the same camera network behind Vancam's road-condition data: over 1 million traffic cameras worldwide, searchable by map bounds, radius, route, or nearest point.

• 🔍 Search cameras by bounding box, radius, route corridor, or nearest-to-point
• 📸 Fetch live frames by camera asset ID, returned directly in the tool result
• 🌐 Real-time data from the same backend that powers the Vancam.ai map
• 🤖 AI-ready — built with FastMCP, works with Claude Desktop, Claude Code, and any MCP-compatible client

Quick Start

Option A: Install from PyPI

uvx vancam-mcp   # or: pip install vancam-mcp

Option B: Clone and install dependencies

git clone https://github.com/shughestr/vancam-mcp.gitcd vancam-mcppip install -r requirements.txt

2. (Optional) Set up an API key

Requests work out of the box using a shared, rate-limited key (1 req/s, 500/month, pooled across all anonymous users). For higher limits, grab a free personal key from your Vancam.ai account page and set it as an environment variable:

cp .env.example .env   # then edit .env

# .envVANCAM_API_KEY=your_personal_key_here

3. Register the server with your MCP client

For Claude Desktop or Claude Code, add to your MCP config. If installed from PyPI (Option A):

{  "mcpServers": {    "vancam": {      "command": "uvx",      "args": ["vancam-mcp"],      "env": {        "VANCAM_API_KEY": ""      }    }  }}

If running from a local clone (Option B, see .mcp.json in this repo):

{  "mcpServers": {    "vancam": {      "command": "python3",      "args": ["/absolute/path/to/vancam-mcp/vancam_mcp/server.py"],      "env": {        "VANCAM_API_KEY": ""      }    }  }}

VANCAM_API_KEY is optional — leave it blank to use the shared, rate-limited key.

Restart your client, and the tools below become available.

Available Tools

list_cameras

List cameras within a bounding box — the same query the VanCam map runs on pan/zoom.

list_cameras(min_lat=49.2, min_lon=-123.2, max_lat=49.3, max_lon=-123.0, limit=50)

get_cameras_by_radius

Get cameras within a radius of a point.

get_cameras_by_radius(lat=49.28, lon=-123.12, radius=1.0, limit=20)

get_cameras_along_route

Get cameras along a straight-line corridor between two points.

get_cameras_along_route(    origin_lat=49.2827, origin_lon=-123.1207,    dest_lat=49.1666, dest_lon=-123.1367,    buffer=200.0, limit=50)

Results are sorted by route_fraction (0 = origin, 1 = destination). Note: this is a straight line between the two points, not a driving route.

get_nearest_cameras

Get the closest cameras to a point.

get_nearest_cameras(lat=49.2827, lon=-123.1207, limit=5)

get_camera_image

Fetch a camera's live frame by asset ID, returned as image data in the tool result (not just a URL — the image endpoint requires an API key header that most MCP clients can't attach themselves).

get_camera_image(asset_id="30145")

describe_camera_api

Returns documentation for all search modes, camera fields, and image URL patterns. Call this first if you're unsure which tool to use.

API Reference

Every search tool queries the same spatial API that backs the Vancam.ai map:

Each camera includes asset_id, latitude, longitude, street_address, direction, camera_class (open/premium), level1/level2/level3 (country/state/city), distance_meters (radius/nearest searches), route_fraction (route search), and image_url/image_urls.

Full schema: openapi.yaml.

Environment overrides: VANCAM_API_KEY, VANCAM_CAMERAS_SEARCH_URL, VANCAM_API_IMAGE_URL

Project Structure

vancam-mcp/├── vancam_mcp/│   ├── server.py      # MCP server — registers the tools above│   └── camera_api.py  # api.vancam.ai client├── openapi.yaml       # API specification├── pyproject.toml     # Package metadata (PyPI: vancam-mcp)├── requirements.txt   # Python dependencies└── .mcp.json          # Example MCP client config

Related Projects

• Vancam.ai — Web interface for traffic cameras
• Model Context Protocol — MCP specification
• Vancam GPT — Same data, packaged as a ChatGPT GPT

Contributing

Contributions are welcome — feel free to open an issue or submit a pull request.

License

MIT — see LICENSE.