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Agent Skill · 研究检索

mapbox-mcp-runtime-patterns(mapbox MCP runtime 模式)

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。

mapbox-mcp-runtime-patterns 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 31,291。

总安装 2,728周安装 116GitHub Stars 公开资料未说明下载 956核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install mapbox-mcp-runtime-patterns;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

mapbox-mcp-runtime-patterns 怎么安装?

在终端执行 openclaw skills install mapbox-mcp-runtime-patterns。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

mapbox-mcp-runtime-patterns 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

mapbox-mcp-runtime-patterns 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

mapbox-mcp-runtime-patterns 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

mapbox-mcp-runtime-patterns 用的人多吗?

公开数据是累计安装 2,728 次,近一周安装 116 次,下载 956 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

mapbox-mcp-runtime-patterns 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
mapbox-mcp-runtime-patterns

mapbox MCP runtime 模式 · 本页

2,728—接触敏感数据MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

平台分布

OpenClaw

73.96%
按下载量换算707

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

可疑

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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