mapbox-mcp-runtime-patterns

name mapbox-mcp-runtime-patterns description Integration patterns for Mapbox MCP Server in AI applications and agent frameworks. Covers runtime integration with pydantic-ai, mastra, LangChain, and custom agents. Use when building AI-powered applications that need geospatial capabilities. Mapbox MCP Runtime Patterns This skill provides patterns for integrating the Mapbox MCP Server into AI applications for production use with geospatial capabilities. What is Mapbox MCP Server? The Mapbox MCP Server is a Model Context Protocol (MCP) server that provides AI agents with geospatial tools: Offline Tools (Turf.js): Distance, bearing, midpoint calculations Point-in-polygon tests Area, buffer, centroid operations Bounding box, geometry simplification No API calls, instant results Mapbox API Tools: Directions and routing Reverse geocoding POI category search Isochrones (reachability) Travel time matrices Static map images GPS trace map matching Multi-stop route optimization Utility Tools: Server version info POI category list Key benefit: Give your AI application geospatial superpowers without manually integrating multiple APIs. Understanding Tool Categories Before integrating, understand the key distinctions between tools to help your LLM choose correctly: Distance: "As the Crow Flies" vs "Along Roads" Straight-line distance (offline, instant): Tools: distance_tool , bearing_tool , midpoint_tool Use for: Proximity checks, "how far away is X?", comparing distances Example: "Is this restaurant within 2 miles?" → distance_tool Route distance (API, traffic-aware): Tools: directions_tool , matrix_tool Use for: Navigation, drive time, "how long to drive?" Example: "How long to drive there?" → directions_tool Search: Type vs Specific Place Category/type search : Tool: category_search_tool Use for: "Find coffee shops", "restaurants nearby", browsing by type Example: "What hotels are near me?" → category_search_tool Specific place/address : Tool: search_and_geocode_tool , reverse_geocode_tool Use for: Named places, street addresses, landmarks Example: "Find 123 Main Street" → search_and_geocode_tool Travel Time: Area vs Route Reachable area (what's within reach): Tool: isochrone_tool Returns: GeoJSON polygon of everywhere reachable Example: "What can I reach in 15 minutes?" → isochrone_tool Specific route (how to get there): Tool: directions_tool Returns: Turn-by-turn directions to one destination Example: "How do I get to the airport?" → directions_tool Cost & Performance Offline tools (free, instant): No API calls, no token usage Use whenever real-time data not needed Examples: distance_tool , point_in_polygon_tool , area_tool API tools (requires token, counts against usage): Real-time traffic, live POI data, current conditions Use when accuracy and freshness matter Examples: directions_tool , category_search_tool , isochrone_tool Best practice: Prefer offline tools when possible, use API tools when you need real-time data or routing. Installation & Setup Option 1: Hosted Server (Recommended) Easiest integration - Use Mapbox's hosted MCP server at: https://mcp.mapbox.com/mcp No installation required. Simply pass your Mapbox access token in the Authorization header. Benefits: No server management Always up-to-date Production-ready Lower latency (Mapbox infrastructure) Authentication: Use token-based authentication (standard for programmatic access): Authorization: Bearer your_mapbox_token Note: The hosted server also supports OAuth, but that's primarily for interactive flows (coding assistants, not production apps). Option 2: Self-Hosted For custom deployments or development: npm install @mapbox/mcp-server Or use directly via npx: npx @mapbox/mcp-server Environment setup: export MAPBOX_ACCESS_TOKEN= "your_token_here" Reference Files Detailed integration patterns and production guidance are organized into reference files. Load the ones relevant to your task. Pydantic AI -- Type-safe Python agents Load: references/pydantic-ai.md CrewAI -- Multi-agent orchestration Load: references/crewai.md Smolagents -- Lightweight HuggingFace agents Load: references/smolagents.md Mastra -- Multi-agent TypeScript systems Load: references/mastra.md LangChain -- Conversational AI with tool chaining Load: references/langchain.md Custom Agent -- Zillow/TripAdvisor/DoorDash-style patterns, architecture diagrams, hybrid approach Load: references/custom-agent.md Use Cases -- Real Estate, Food Delivery, Travel Planning examples Load: references/use-cases.md Production Patterns -- Caching, batch operations, tool descriptions, error handling, security, rate limiting, testing Load: references/production.md Resources Mapbox MCP Server Model Context Protocol Pydantic AI Mastra LangChain Mapbox API Documentation When to Use This Skill Invoke this skill when: Integrating Mapbox MCP Server into AI applications Building AI agents with geospatial capabilities Architecting Zillow/TripAdvisor/DoorDash-style apps with AI Choosing between MCP, direct APIs, or SDKs Optimizing geospatial operations in production Implementing error handling for geospatial AI features Testing AI applications with geospatial tools