What is Amap Mcp Demo

amap-mcp-demo is a demonstration project that showcases the usage of all available Amap MCP APIs in a Node.js environment. It includes both frontend and backend components for testing various API endpoints.

Use cases

Use cases for amap-mcp-demo include developing applications for route planning, location-based services, weather information retrieval, and enhancing user experiences in navigation and travel applications.

How to use

To use amap-mcp-demo, install the necessary dependencies using ‘npm install’, configure your Amap MCP API key in the .env file, start the server with ‘npm start’, and access the application via your browser at http://localhost:3000.

Key features

Key features of amap-mcp-demo include geocoding, reverse geocoding, IP location, POI search, POI details, weather queries, distance calculations, route planning for various modes of transport, and personal map customization.

Where to use

amap-mcp-demo can be used in various fields such as transportation, logistics, urban planning, tourism, and any application that requires mapping and location-based services.

Amap MCP Demo (Node.js + JS)

This project demonstrates how to use all available Amap (高德地图) MCP APIs in a Node.js environment, with a simple frontend for interaction.

Features & APIs Covered

• Geocoding (address to coordinates)
• Reverse Geocoding (coordinates to address)
• IP Location
• POI Search (text and around)
• POI Detail
• Weather Query
• Distance Calculation
• Route Planning (driving, walking, cycling, transit)
• Schema (open Amap app for navigation/taxi)
• Personal Map (custom route display)

Project Structure

amap-mcp-demo/
  ├── package.json
  ├── README.md
  ├── .env.example
  ├── server.js
  ├── public/
  │     ├── index.html
  │     └── main.js
  └── src/
        ├── amapClient.js   // All MCP API calls
        └── routes.js       // Express routes for each API

Setup

1. Install dependencies:

   npm install
   

2. Copy .env.example to .env and fill in your Amap MCP API key.
3. Start the server:

   npm start
   

4. Open http://localhost:3000 in your browser.

How to Use

• Each API is demonstrated via a simple web interface.
• You can test all endpoints directly from the browser or via API calls.

What is Model Context Protocol (MCP)?

Model Context Protocol (MCP) is an open protocol designed to enable AI models (such as Claude, GPT, etc.) to securely and standardly access external data sources, tools, and services.

• MCP is not a proprietary protocol of any single company, but an open standard promoted by AI companies like Anthropic.
• The core idea is to let AI models access various “external capabilities” (such as databases, APIs, maps, files, web, etc.) through a standard interface, just like humans can.

MCP Server and MCP Client

• MCP Server: A provider of capabilities/data/tools that implements the MCP protocol and can be accessed by AI models via MCP Clients. Examples include: Amap Maps MCP Server, Github MCP Server, Redis MCP Server, Blender MCP Server, etc.
• MCP Client: The agent for AI models, which can call these servers via the MCP protocol. Examples include: Claude, Cursor, Windsurf, ChatWise, etc.

Why does AI need MCP?

• Traditional AI models can only work with their own knowledge and reasoning, unable to access real-time external information.
• With MCP, AI models can “connect to the internet,” use tools, query databases, access maps, send emails, and more—greatly expanding their capabilities.
• This turns AI into a “super plugin platform” or “digital operator.”

Amap Maps MCP Server and AI

• The Amap Maps MCP Server wraps Amap’s API capabilities as an MCP protocol service for AI models to call.
• This allows AI models (like Claude, GPT, etc.) to use natural language to query routes, POIs, perform geo-analyses, etc., via the MCP protocol.
• Thus, AI models gain real-time map capabilities in a standardized, composable way.

The Significance of mcp.so

• mcp.so is a marketplace or capability hub for MCP Servers and Clients.
• You can find various MCP Servers (Amap Maps, Github, Redis, Blender, Figma, Baidu Map, etc.) and MCP Clients (Cursor, Claude, Windsurf, etc.).
• This shows that MCP is becoming the “capability interconnection standard” in the AI ecosystem, enabling seamless collaboration between AI models and external services.

Summary: The Relationship Between MCP and AI

• MCP is the bridge for AI models to connect with the real world, enabling AI to not only “chat” but also “do things.”
• Future AI assistants, agents, and operating systems will use the MCP protocol to call various servers, becoming “super digital employees.”
• You can think of MCP as the “USB interface for AI”—as long as it supports MCP, AI can plug into all kinds of capability “peripherals.”

References:

• MCP.so Official Website
• MCP FAQ (English) (see the bottom of the page)

This project is for learning and demonstration purposes. For production use, please secure your API keys and follow best practices.