How do I use Vancam MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Vancam MCP Server find traffic cameras near downtown Vancouver" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Vancam MCP Server

by shughestr

Overview Schema Source Related Servers Score Discussions Admin

Python

Remote

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.git
cd vancam-mcp
pip 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

# .env
VANCAM_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.

Related MCP server: ThinAir Geo

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)

Parameter	Type	Description
`min_lat`, `min_lon`, `max_lat`, `max_lon`	float	Bounding box (WGS84)
`limit`	int, optional	Max results, 1–100 (default 100)
`active_only`	bool, optional	Only `camera_class=open` live feeds (default false)

`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)

Parameter	Type	Description
`lat`, `lon`	float	Center point (WGS84)
`radius`	float, optional	Radius in km (default 1.0)
`limit`	int, optional	Max results (default 50)
`active_only`	bool, optional	Only open/live cameras

`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
)

Parameter	Type	Description
`origin_lat`, `origin_lon`, `dest_lat`, `dest_lon`	float	Route endpoints
`buffer`	float, optional	Corridor width in meters (default 100.0)
`limit`	int, optional	Max results (default 50)
`active_only`	bool, optional	Only open/live cameras

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)

Parameter	Type	Description
`lat`, `lon`	float	Query point (WGS84)
`limit`	int, optional	Number of cameras (default 5)
`active_only`	bool, optional	Only open/live cameras

`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:

Purpose	URL
Spatial search	`https://api.vancam.ai/cameras/cameras`
Live image	`https://api.vancam.ai/api?asset_id={id}`

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

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.

Available Tools

6 tools

describe_camera_apiA

Describe the spatial camera API exposed by this MCP server.

Call this first when unsure which search tool to use. Documents the same capabilities as the VanCam map web app (bounds pan, radius, route, nearest).

ParametersJSON Schema

Name	Required	Description	Default
No parameters

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.7/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Since no annotations are provided, the description carries the full behavioral burden. It discloses that the tool is a documentation/informational tool and specifies the feature set it covers. There is no unsafe or unexpected behavior implied, which is appropriate for a 'describe' tool, though it does not elaborate on response format or side effects beyond the output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, with the first defining the purpose and the second giving usage guidance. It is front-loaded, concise, and contains no filler, making it highly efficient for an agent to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has no parameters and an output schema exists, the description is complete: it states what the tool does, when to call it, and what its documentation covers. The sibling tool names are provided in context, so the agent is well-oriented without missing critical information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so there are no parameter semantics to explain. The baseline of 4 applies, and the description appropriately focuses on use case rather than argument syntax, since the schema is empty.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Describe the spatial camera API exposed by this MCP server,' giving a specific verb and resource. It distinguishes itself from the sibling search tools by positioning itself as an API reference/entry point, not a searching tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'Call this first when unsure which search tool to use' provides explicit when-to-use guidance. It also clarifies scope by naming the capabilities it documents (bounds pan, radius, route, nearest), which helps the agent decide if this is the right entry point.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_camera_imageA

Get the live frame for a camera by asset_id, embedded directly in the tool result.

api.vancam.ai requires an x-api-key header this server holds — an MCP client fetching the bare URL itself (e.g. Claude Desktop opening the link) has no way to attach that header and gets 403 Forbidden. This tool fetches the frame server-side instead and returns the image bytes.

Parameters: asset_id: Vancam camera asset ID (integer as string)

Returns: The live image, plus asset_id/url metadata for reference.

ParametersJSON Schema

Name	Required	Description	Default
`asset_id`	Yes

TDQS

A4.6/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the transparency burden and does well by disclosing the API auth mechanism, the 403 failure mode, and that the tool returns image bytes rather than a URL. It reveals important server-side behavior that an agent would otherwise not know.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the purpose, then provides only essential context about the auth requirement, followed by a brief parameters and returns section. Every sentence contributes meaningful information without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter tool with no output schema, the description fully covers the input meaning, the return value, and why the tool exists. The sibling tools provide camera discovery context, so this description is complete on its own.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description compensates fully: it explains that asset_id is a Vancam camera asset ID and notes it should be provided as an integer represented as a string. This is exactly the meaning an agent needs beyond the raw schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: "Get the live frame for a camera by asset_id," clearly distinguishing this tool from sibling camera-listing tools. It also specifies the unique return (live image bytes) and mentions the camera asset ID as input.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains when this tool is needed: it fetches the frame server-side because a bare URL fetch would result in 403 Forbidden due to the required x-api-key header. This gives clear context for using the tool, though it does not explicitly name alternatives or state exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_cameras_along_routeA

Get traffic cameras along a straight-line route between two points.

Same API call as the VanCam web app (vancam.ai) route view. Cameras are returned sorted by route_fraction (0 = origin, 1 = destination).

Note: Uses a straight line between origin and destination (PostGIS ST_MakeLine), not a driving route polyline. Buffer is meters perpendicular to that line.

Parameters: origin_lat, origin_lon: Route start (WGS84) dest_lat, dest_lon: Route end (WGS84) buffer: Corridor width in meters (default 100, web app uses 100) limit: Max cameras (1–100, default 50, web app uses 50) active_only: If true, only camera_class=open

Returns: JSON with type route_search, buffer_meters, cameras[] with route_fraction.

ParametersJSON Schema

Name	Required	Description	Default
`limit`	No
`buffer`	No
`dest_lat`	Yes
`dest_lon`	Yes
`origin_lat`	Yes
`origin_lon`	Yes
`active_only`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.7/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description discloses the straight-line corridor behavior, perpendicular buffer, sorting by route_fraction, and return structure. It also mentions default buffer/limit values and the active_only filtering semantics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with a summary, caveat, parameter details, and return section. Slightly redundant with the schema's parameter list, but each sentence contributes and the organization helps scanability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 7-parameter tool with no annotations and a moderate output schema, the description covers behavior, key constraints (straight-line vs driving), result ordering, and output fields. It is complete for an agent to select and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Adds meaning beyond the schema: WGS84 coordinates, buffer in meters perpendicular to the line, limit range 1–100, and active_only mapping to camera_class=open. Schema only provides names and defaults.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states 'Get traffic cameras along a straight-line route between two points' – a specific verb and resource that distinguishes it from radius/nearest/list tools. The straight-line qualifier is explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Notes it mirrors the VanCam route view and explicitly states it uses a straight line, not a driving route polyline. This tells users when to avoid expecting driving-route corridors, though it doesn't name alternative tools directly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_cameras_by_radiusA

Get traffic cameras within a radius (km) of a lat/lon point.

Radius search. Use when the user asks for cameras near a place, within X km of coordinates, or around an address they geocoded.

Parameters: lat, lon: Center point (WGS84) radius: Search radius in kilometers (default 1.0, max 50 per API) limit: Max cameras (1–100, default 50) active_only: If true, only camera_class=open

Returns: JSON with type radius_search, count, radius_km, cameras[].

ParametersJSON Schema

Name	Required	Description	Default
`lat`	Yes
`lon`	Yes
`limit`	No
`radius`	No
`active_only`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.6/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full transparency burden. It discloses important behavior such as the maximum radius ('max 50 per API'), the meaning of active_only ('only camera_class=open'), and the JSON return structure ('type radius_search, count, radius_km, cameras[]'). This goes beyond the schema and gives the agent actionable expectations, though it stops short of covering error cases or sorting details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured: a one-sentence summary, a usage line, a parameter list, and a return format. No fluff or redundancy; every section serves a purpose and information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (5 params, output schema present), the description provides complete operational context: purpose, when to use, parameter semantics, and return shape. The output schema covers detailed field structure, so the description need not repeat it, but it still gives a high-level return summary. It is fully adequate for an agent to select and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must provide parameter meaning. It does so thoroughly: lat/lon identified as center point with WGS84, radius with units/default/max, limit with range/default, and active_only with a clear filter explanation. This fully compensates for the schema's lack of descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description opens with 'Get traffic cameras within a radius (km) of a lat/lon point,' which clearly states the action, resource, and scope. It distinguishes from sibling tools by focusing on radius-based search, setting it apart from list_cameras or get_nearest_cameras.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool: 'Use when the user asks for cameras near a place, within X km of coordinates, or around an address they geocoded.' This provides clear trigger contexts, though it does not explicitly mention alternatives or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_nearest_camerasA

Get the nearest traffic cameras to a geographic point.

Nearest-neighbor search. Use when the user asks for the closest camera(s) to a location.

Parameters: lat, lon: Query point (WGS84) limit: Number of cameras (1–100, default 5) active_only: If true, only camera_class=open

Returns: JSON with type nearest_search and cameras[] ordered by distance_meters.

ParametersJSON Schema

Name	Required	Description	Default
`lat`	Yes
`lon`	Yes
`limit`	No
`active_only`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.6/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses behavioral traits such as nearest-neighbor search, the active_only filtering (camera_class=open), and the ordering by distance_meters. It does not mention auth or rate limits, but for a read-only query tool these are not critical gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and structured with purpose, usage, parameters, and return sections. Every sentence adds value; the front-loaded purpose and bullet-like parameter list make it easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity, four parameters, and an output schema, the description covers all necessary context: when to use, what parameters mean, and what the response contains. The sibling tool names clarify the alternative spatial queries, making the tool complete within its context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All four parameters are explained beyond the schema: lat/lon as query point in WGS84, limit with range 1–100 and default 5, active_only with the meaning of filtering to camera_class=open. Schema coverage is 0%, so this description fully compensates.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Get the nearest traffic cameras to a geographic point,' which clearly states the verb (get), resource (traffic cameras), and scope (nearest to a point). It distinguishes itself from siblings like 'get_cameras_by_radius' and 'get_cameras_along_route' by focusing on nearest-neighbor semantics.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use when the user asks for the closest camera(s) to a location,' providing a clear when-to-use context. It does not list exclusions or alternative tools, but the context is specific enough to guide selection among sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_camerasA

List traffic cameras within a map bounding box.

Same query the VanCam web app (vancam.ai) runs when the user pans/zooms the map. Use when the user asks for cameras in a visible map area or geographic rectangle.

Parameters: min_lat, min_lon, max_lat, max_lon: Bounding box (WGS84) limit: Max cameras to return (1–100, default 100) active_only: If true, return only camera_class=open (live feeds)

Returns: Bounds search JSON with enriched image_url per camera.

ParametersJSON Schema

Name	Required	Description	Default
`limit`	No
`max_lat`	Yes
`max_lon`	Yes
`min_lat`	Yes
`min_lon`	Yes
`active_only`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.6/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden. It discloses the return format ('Bounds search JSON with enriched image_url per camera') and the behavior of active_only, adding useful context. It does not mention any side effects, but the operation is inherently read-only.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and well-structured: purpose, usage, parameter list, and return type. Every sentence contributes value, with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given an output schema exists, the description covers all necessary context: purpose, usage, parameters, and return shape. It is complete for a moderately complex tool even without annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description fully documents all parameters: 'min_lat, min_lon, max_lat, max_lon: Bounding box (WGS84)', 'limit: Max cameras to return (1–100, default 100)', and 'active_only: If true, return only camera_class=open'. This adds complete meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'List traffic cameras within a map bounding box,' which clearly states the verb, resource, and scope. It distinguishes itself from sibling tools like get_cameras_by_radius by specifying the bounding-box query type.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly provides a usage trigger: 'Use when the user asks for cameras in a visible map area or geographic rectangle.' However, it does not mention when not to use or name alternatives directly, though the sibling context implies them.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

6 tool updatesv0.1.0Aug 13, 2026
- First observeddescribe_camera_api
- First observedget_camera_image
- First observedget_cameras_along_route
- First observedget_cameras_by_radius
- First observedget_nearest_cameras
- First observedlist_cameras

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A4.7/5.0

Scored 2026-08-14 00:05 across 6 tools

Disambiguation5/5

Each tool targets a distinct spatial query mode (bounds, radius, route, nearest) plus image retrieval and API documentation. There is no overlap; an agent can easily select the right tool based on the user's query.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case, with 'camera(s)' as the object. The verbs list, get, and describe are all clear and predictable, making the naming internally consistent.

Tool Count5/5

Six tools is an appropriate size for a camera discovery and retrieval server. Each spatial query type is represented, plus image fetching and a meta-documentation tool, with no unnecessary bloat.

Completeness5/5

The tool set covers all spatial search modes from the VanCam web app (bounding box, radius, route corridor, nearest neighbor) and includes live image retrieval that works around the API key limitation. There are no obvious gaps for the stated purpose.

Maintenance

ActivitySlowing

ResponsivenessNo issues

Resources

GitHub Repository

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Provides access to real-time Danish road traffic events, roadworks, and queues via public data from Vejdirektoratet, enabling AI assistants to check road conditions without any API keys.
Updated 3 months ago (2026-06-12 22:37 UTC)
3
MIT

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Vancam MCP Server

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list_cameras

get_cameras_by_radius

get_cameras_along_route

get_nearest_cameras

get_camera_image

describe_camera_api

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MCP directory API

`list_cameras`

`get_cameras_by_radius`

`get_cameras_along_route`

`get_nearest_cameras`

`get_camera_image`

`describe_camera_api`