Simple MCP Weather STDIO Server
by xiaobo187
This is a simple example project demonstrating how to build an MCP Server. It uses the Gaode Open Platform's weather API to provide real-time and future weather information.
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What is Simple MCP Weather STDIO Server?
This MCP server provides weather information based on location coordinates. It utilizes the Gaode Open Platform's weather API to fetch weather data and returns it to the AI client.
How to use Simple MCP Weather STDIO Server?
- Build the server using Maven (
./mvnw clean install -DskipTests
). 2. Configure the server in an AI client like Cherry Studio by specifying the STDIO transport mode and the path to the built JAR file. 3. Set theGAODE_API_KEY
environment variable with your Gaode API key. 4. Enable the MCP server in the AI client and use it in conversations.
Key features of Simple MCP Weather STDIO Server
Real-time weather data
Future weather forecasts
Integration with Gaode Open Platform
STDIO transport mode
Easy integration with AI clients
Function Call support
Use cases of Simple MCP Weather STDIO Server
Answering user questions about the weather in a specific location
Providing weather information to AI assistants for context-aware responses
Integrating weather data into AI-powered applications
Automating weather-related tasks
FAQ from Simple MCP Weather STDIO Server
What is MCP?
What is MCP?
MCP stands for Machine Communication Protocol, a protocol for AI agents to communicate with external tools and services.
What is Gaode Open Platform?
What is Gaode Open Platform?
Gaode Open Platform is a Chinese mapping and navigation service provider offering various APIs, including a weather API.
What AI clients are supported?
What AI clients are supported?
Any AI client that supports the MCP protocol can be used. The example uses Cherry Studio.
What kind of API key do I need from Gaode?
What kind of API key do I need from Gaode?
You need a Web API key from the Gaode Open Platform.
What large language model should I use?
What large language model should I use?
It is recommended to use a large language model with Function Call
functionality. For example, Qwen2.5-7B-Instruct
.