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mapbox-mcp-runtime-patternsmapbox MCP runtime 模式

Agent Skill

mapbox-mcp-runtime-patterns 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

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周安装

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下载量

956
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:mapbox-mcp-runtime-patterns(mapbox MCP runtime 模式)
来源仓库:https://github.com/mapbox/mapbox-mcp-runtime-patterns
安装命令:
openclaw skills install mapbox-mcp-runtime-patterns
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install mapbox-mcp-runtime-patterns

简介

Mapbox MCP运行时集成方案,支持LangChain与pydantic-ai等主流框架。

  • 适用于AI应用地理空间推理、路径规划与区域语义理解任务。
  • 提供异步查询接口、缓存策略与错误重试机制封装组件。
  • 需监控API调用频次与配额使用情况,防止超出免费额度限制。
  • 建议实现本地缓存兜底逻辑,在网络不可用时返回最近可用数据。

SKILL.md

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

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

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

73.96%
按下载量换算707

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可疑

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可疑

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安装前确认

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