Introducing the Mapbox MCP Server: Location Intelligence for AI Agents

Source: Dev.to

Introduction

AI agents are getting smarter every day – they can write code, analyze data, and answer complex questions.
But when it comes to understanding where things are, most agents hit a wall. They can tell you about restaurants in San Francisco, but they can’t:

• Geocode an address
• Calculate a route
• Determine if two locations are within walking distance

That’s where the Mapbox Model Context Protocol (MCP) Server comes in. It gives AI agents access to Mapbox’s powerful location services through a simple, standardized interface. No custom API integration is required – just connect your agent to the MCP server and it can instantly:

• Geocode addresses
• Plan routes
• Analyze travel times
• Create maps

Example

An AI agent built with MCP can naturally handle location queries and display results visually.

What Is the Model Context Protocol?

The Model Context Protocol is an open standard created by Anthropic for connecting AI models to external tools and data sources. Think of it as USB for AI agents – a universal connector that works across different frameworks and platforms.

Instead of writing custom code for each API, you:

1. Connect your agent to an MCP server
2. The server exposes its tools in a standardized format
3. Your agent discovers and uses those tools automatically

MCP is supported by popular frameworks such as CrewAI, LangGraph, Pydantic AI, and Mastra, as well as AI assistants like GitHub Copilot and Claude Desktop.

Mapbox MCP Server – Geospatial Tools

The Mapbox MCP Server exposes nine powerful geospatial tools that give your agents comprehensive location intelligence.

Highlights

• Traffic‑Aware Routing – directions_tool supports real‑time traffic, alternative routes, and multiple transportation modes (driving, walking, cycling).
• Multi‑Point Optimization – matrix_tool efficiently calculates travel times for dozens of locations – essential for logistics and service‑area analysis.
• Reachability Analysis – isochrone_tool generates polygons showing areas reachable within specific time or distance thresholds.
• Visual Output – static_image_tool creates customizable map images with markers, routes, and overlays – perfect for reports or notifications.

Using the Mapbox MCP Server

You have two ways to use the server:

1. Hosted MCP Endpoint

Mapbox provides a hosted MCP endpoint that’s always available:

https://mcp.mapbox.com/mcp

Authentication

Setup – Interactive Tools

Add the following to your MCP settings (usually settings.json or a similar config file):

{
  "mcpServers": {
    "mapbox": {
      "url": "https://mcp.mapbox.com/mcp"
    }
  }
}

Claude Desktop config locations

The first time you connect, a browser window will open asking you to log in to your Mapbox account and authorize access via OAuth.

Setup – Programmatic Access

1. Create a Mapbox account (free tier) – Sign up here.
2. Generate an access token from your account dashboard.

Use the token as a Bearer token in your code (see framework‑specific tutorials for examples).

Reference: Hosted MCP Server Guide – detailed setup for different clients.

Pros & Cons

2. Run MCP Server Locally

Running the server locally gives you full control – ideal for development, debugging, or customization.

Prerequisites

• Node.js LTS+ (for npx method) or Python 3.10+ with uv (for uvx method)
• A Mapbox account (free tier) – Sign up here
• Your Mapbox access token

Setup – Interactive Tools

Add the following to your MCP settings:

{
  "mcpServers": {
    "mapbox": {
      "command": "npx",
      "args": ["-y", "@mapbox/mcp-server"],
      "env": {
        "MAPBOX_ACCESS_TOKEN": "your_token_here"
      }
    }
  }
}

Claude Desktop config locations

Running the Server (Node)

npx -y @mapbox/mcp-server

Running the Server (Python/uv)

uvx @mapbox/mcp-server

Pros & Cons

Note on the DevKit MCP Server

Mapbox also offers the DevKit MCP Server (hosted at https://mcp-devkit.mapbox.com/mcp). It is designed to help developers build Mapbox applications by providing:

• Code examples
• Documentation search
• Development assistance

This post focuses on the main MCP server for location intelligence. The DevKit will be covered in a future post.

Quick‑Start Guide

Configuration File

Running the Server (Command‑Line)

1. Set your Mapbox access token

   export MAPBOX_ACCESS_TOKEN="your_token_here"

2. Start the server

   Node.js (recommended)

   npx -y @mapbox/mcp-server

   Python (via uvx)

   pip install uv          # if you don’t have uv yet
   uvx mapbox-mcp

3. Test with MCP Inspector

   npx @modelcontextprotocol/inspector npx -y @mapbox/mcp-server

   A web UI opens, letting you explore the exposed tools and try them interactively.

Sample Natural‑Language Requests

The agent will automatically discover the right tools, call them, and return a concise answer (no manual API handling required).

Compatibility – MCP‑Ready Clients

All follow the same basic flow:

1. Configure the MCP server connection.
2. Add the configuration to your agent.
3. The agent discovers available tools automatically.
4. Use natural language to invoke tools.

Detailed tutorials for each framework are coming soon.

Why Use MCP Instead of a Custom API Integration?

Real‑World Use Cases

• Hotel concierge bots (directions, local recommendations)
• Trip‑planning assistants (multi‑stop itinerary optimization)
• Event coordinators (travel‑time calculations for attendees)
• Delivery‑fleet route optimization
• Service‑area analysis for expansion planning
• Warehouse location analysis (customer distribution)
• Commute‑time calculators for real‑estate listings
• School‑district & amenity proximity analysis
• Neighborhood boundary visualizations
• Technician dispatch optimization
• Emergency‑response time calculations
• Store locators with accurate directions
• Coverage‑area mapping for service offerings
• Competitive location analysis

Resources

• Mapbox MCP Server – GitHub (source & docs)
• Hosted MCP Server Guide – Setup for different clients
• Model Context Protocol (MCP) Spec – Standard documentation
• Mapbox API Docs – Underlying API reference
• Support – mcp-feedback@mapbox.com
• Issues – GitHub Issues
• Community – Join discussions in the MCP community

Pricing (Standard Mapbox API Rates)

The free tier is generous for development and small projects. For production, review the full Mapbox pricing page.

Get Started

1. Sign up for Mapbox – Get a free access token at the sign‑up page.
2. Run the server (see “Running the Server” above).
3. Try the MCP Inspector to explore tools.
4. Pick a framework and start building location‑aware AI agents!

Happy mapping! 🚀

Getting Started

• Start with CrewAI, LangGraph, or your preferred agent framework.
• Build something: Check out our framework‑specific tutorials (coming soon!).

What’s Next

• In our next post, we’ll dive deep into building a multi‑agent travel planning system with CrewAI and Mapbox MCP, complete with working code you can run and customize.

Coming Soon

• A guide to the Mapbox DevKit MCP Server, which helps AI assistants write Mapbox code by providing documentation, examples, and API guidance—perfect for developers building Mapbox applications.

Get In Touch

• Have questions or want to share what you’re building? Reach out to mcp-feedback@mapbox.com or open an issue on GitHub.