Mapbox MCP Server: Integration Tool for AI Applications via Model Context Protocol

Overview: What is the Mapbox MCP Server?

The Mapbox MCP Server is a specialized component within the broader ecosystem of Model Context Protocol (MCP) servers, aiming to provide essential geospatial and routing functionalities through a standardized interface that enables seamless integration with various AI applications. This server is designed for developers looking to enhance their AI workflows by leveraging powerful mapping features such as directions, matrix calculations, and geocoding.

🔧 Core Features & MCP Capabilities

The Mapbox MCP Server offers several key features integral to a wide range of AI application integrations:

Navigation Tools

1. mapbox_directions

• Purpose: Provides detailed routing information between specified coordinates.
• Inputs:
  • coordinates: Array of coordinate pairs (latitude, longitude).
  • profile (optional): Specifies the transport mode like "driving", "walking", or "cycling".
• Output: Route details, distance, and duration.

2. mapbox_directions_by_places

• Purpose: Calculates directions between places using their names.
• Inputs:
  • places: An array of place names (e.g., city, landmark).
  • profile (optional): Specifies the transport mode like "driving", "walking", or "cycling".
  • language (optional): Used to filter results based on the specified language.
• Output: Geocoding information for each place, route details with steps, distance, and duration. Errors include in the response.

3. mapbox_matrix

• Purpose: Provides a matrix of travel time and distance between given coordinates.
• Inputs:
  • coordinates: Array of coordinate pairs (latitude, longitude).
  • profile (optional): Specifies the transport mode like "driving", "walking", or "cycling".
  • annotations (optional): Additional information to include in the matrix response like "duration" and/or "distance".
  • sources (optional): Indices of the source coordinates.
  • destinations (optional): Indices of the destination coordinates.
• Output: Matrix containing travel times and distances between selected points.

4. mapbox_matrix_by_places

• Purpose: Generates a matrix for multiple places using their names.
• Inputs:
  • places: Array of place names up to 25.
  • profile (optional): Specifies the transport mode like "driving", "walking", or "cycling".
  • annotations (optional): Additional annotation types such as "duration" and/or "distance".
  • language (optional): Used to provide results in a specific language.
  • sources (optional): Indices of source places.
  • destinations (optional): Indices of destination places.
• Output: Geocoding information for each place, matrix containing travel times and distances.

Search Tools

5. mapbox_geocoding

• Purpose: Translates addresses or place names into geographic coordinates.
• Inputs:
  • searchText: The text to be geocoded (address or place name).
  • limit (optional): Limits the number of returned results between 1 and 10.
  • types (optional): Filters search results by specific types such as country, region, or city.
  • language (optional): Specifies the language for fuzzy matching.
  • fuzzyMatch: Boolean enabling or disabling fuzzy matching.
• Output: Detailed location information including coordinates and properties.

⚙️ MCP Architecture & Protocol Implementation

The Mapbox MCP Server follows a structured architecture to ensure compatibility with various Model Context Protocol clients. This involves several key components:

Directory Structure

src/
├── types/          # Type definitions for inputs and outputs.
├── schemas/        # Zod schemas to validate input data.
├── tools/
│   ├── definitions/  # Tool definitions for geospatial operations.
│   └── handlers/     # Handler implementations for each tool provided by the server.
└── server/
    └── handlers/     # Handler classes that implement the protocol logic. Each feature module follows this structure.

Base Class for Handlers

• src/server/handlers/base.ts: Encapsulates base handling for all MCP services, ensuring consistency in input validation and error reporting.

Registry of Handlers

• src/server/registry.ts: Maintains a list of available handlers, allowing dynamic loading based on client requests.

Main Entry Point

• src/server/main.ts: The main entry point of the server. Initializes the environment, starts the server, and ensures all necessary dependencies are loaded properly.

🚀 Getting Started with Installation

To set up the Mapbox MCP Server, follow these steps:

1. Install Required Dependencies:

   • Node.js 16 or above.
   • Node version manager (nvm) for easy node installation management.

2. Clone and Install the Project:

   git clone https://github.com/your-repo-mapbox-mcp-server.git
   cd mapbox-mcp-server
   npm install
   

3. Prepare Your Environment Variables:

   export MAPBOX_ACCESS_TOKEN=your_api_key_here
   

4. Start the Server:

   • Modify your claude_desktop_config.json if integrating with Claude Desktop.

   {
     "mcpServers": {
       "mapbox-mcp-server": {
         "command": "node",
         "args": ["/absolute/path/to/mapbox-mcp-server/build/index.js"],
         "env": {
           "MAPBOX_ACCESS_TOKEN": "your-api_key_here"
         }
       }
     }
   }
   

5. Run the Server:

   node /absolute/path/to/mapbox-mcp-server/build/index.js
   

💡 Key Use Cases in AI Workflows

Real-time Traffic Monitoring

Suppose an AI application requires real-time traffic updates to provide efficient route recommendations for users. By integrating the mapbox_directions API, this can be achieved by querying live traffic information from Mapbox on behalf of the user.

graph TD;
A[AI Application] -->|API request| B[MCP Client]
B --> C[Mapbox MCP Server]
C --> D[Traffic Data Source]
D --> E[CALCULATE ROUTES]
E --> F[CALCULATE RISKS]
F --> G[SUGGEST ALTERNATE PATHS]
G --> H[AI Application Response]

Geolocation and Smart Routing

An AI-driven smart delivery application needs to optimize routes for parcel delivery. By leveraging the mapbox_matrix API, it can generate time matrices between multiple warehouse locations and customer addresses, improving route planning efficiency and reducing operational costs.

🔌 Integration with MCP Clients

The Mapbox MCP Server is fully compatible with several mainstream AI applications:

• Claude Desktop: Supports full-fledged integration.
• Continue: Provides robust support across tools.
• Cursor: Limited capabilities; handles only geospatial queries but not prompts.

MCP Client Compatibility Matrix

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

📊 Performance & Compatibility Matrix

The server offers the following performance and compatibility details:

• Directions API: 300 requests per minute.
• Matrix API:
  • Driving/Walking/Cycling: 60 requests per minute
  • Driving-Traffic: 30 requests per minute
• Geocoding API: 600 requests per minute.

🛠️ Advanced Configuration & Security

Environment and Configuration Management

Customize the server's environment settings via claude_desktop_config.json or custom .env files. The following sample shows how to configure an MCP client.

{
  "mcpServers": {
    "mapbox-mcp-server": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-mapbox"],
      "env": {
        "API_KEY": "your_api_key_here"
      }
    }
  }
}

Security Measures

• Ensure the MAPBOX_ACCESS_TOKEN is stored securely.
• Implement rate limiting to prevent abuse of API endpoints.

❓ Frequently Asked Questions (FAQ)

1. Q: How can I change the language for geocoding results?

   • Set the language parameter in your client request when using mapbox_geocoding.

2. Q: What if my client does not support matrix calculations?

   • Matrix API tools like mapbox_matrix require proper client support due to their complex nature.

3. Q: Can I use this server with Cursor?

   • Yes, but limited to geospatial queries. Prompts and other interactive features are not supported.

4. Q: What should I do if I encounter rate limiting issues?

   • Adjust your request frequency or contact Mapbox support for additional quota.

5. Q: How can I debug issues with the server?

   • Use console logs during development or implement custom logging mechanisms as needed.

Conclusion

The Mapbox MCP Server represents a robust solution for integrating geospatial and routing functionalities into various AI applications, ensuring seamless interaction through standardized Model Context Protocol interfaces. With its comprehensive feature set and compatibility across multiple client platforms, it stands out as an essential component in modern AI-driven geolocation solutions.