Simple MCP Server C51
A simple example project of an MCP Server that implements weather query and message push functions through the Gaode Weather API and DingTalk robot
rating : 2.5 points
downloads : 21
What is Simple MCP Server?
Simple MCP Server is a demonstration project that shows how to build a Model Context Protocol (MCP) server. It integrates the Gaode Weather API and DingTalk group robot functions, enabling AI assistants to obtain real-time weather information and send messages to DingTalk groups.How to use Simple MCP Server?
Connect to the server through an AI client that supports the MCP protocol, such as Cherry Studio, and the AI assistant can automatically call the weather query or DingTalk message function.Applicable scenarios
Suitable for scenarios where an AI assistant needs to obtain real-time weather information or send notifications to a team group.Main features
Weather queryObtain real-time weather and weather forecast information through the Gaode Weather API
DingTalk group messageSend messages to a specified group through the DingTalk group robot
Advantages and limitations
Advantages
Simple and easy to use, quick to deploy
Supports mainstream AI clients
Practical functions that cover common needs
Limitations
Requires relying on third-party API services
Relatively basic functions
Requires a large model that supports Function Call
How to use
Environment preparation
Install Node.js 22+ and Pnpm, and apply for a Gaode API key and a DingTalk robot access token
Build the project
Clone the project, install dependencies, and then execute the build
Configure the MCP server
Add the MCP server in Cherry Studio and set environment variables
Usage examples
Query the weatherAsk the AI assistant about the weather in a certain place
Send a DingTalk messageAsk the AI assistant to send information to the DingTalk group
Frequently asked questions
Why doesn't the AI call the MCP function?
How to obtain an API key?
Related resources
Gaode Weather API documentation
Instructions for using the Gaode Weather API
DingTalk robot access documentation
Guide for accessing the DingTalk custom robot
Cherry Studio official website
An AI client that supports the MCP protocol
Featured MCP Services

Notion Api MCP
Certified
A Python-based MCP Server that provides advanced to-do list management and content organization functions through the Notion API, enabling seamless integration between AI models and Notion.
Python
141
4.5 points

Gitlab MCP Server
Certified
The GitLab MCP server is a project based on the Model Context Protocol that provides a comprehensive toolset for interacting with GitLab accounts, including code review, merge request management, CI/CD configuration, and other functions.
TypeScript
86
4.3 points

Markdownify MCP
Markdownify is a multi-functional file conversion service that supports converting multiple formats such as PDFs, images, audio, and web page content into Markdown format.
TypeScript
1.7K
5 points

Duckduckgo MCP Server
Certified
The DuckDuckGo Search MCP Server provides web search and content scraping services for LLMs such as Claude.
Python
830
4.3 points

Unity
Certified
UnityMCP is a Unity editor plugin that implements the Model Context Protocol (MCP), providing seamless integration between Unity and AI assistants, including real - time state monitoring, remote command execution, and log functions.
C#
566
5 points

Figma Context MCP
Framelink Figma MCP Server is a server that provides access to Figma design data for AI programming tools (such as Cursor). By simplifying the Figma API response, it helps AI more accurately achieve one - click conversion from design to code.
TypeScript
6.7K
4.5 points

Minimax MCP Server
The MiniMax Model Context Protocol (MCP) is an official server that supports interaction with powerful text-to-speech, video/image generation APIs, and is suitable for various client tools such as Claude Desktop and Cursor.
Python
754
4.8 points

Context7
Context7 MCP is a service that provides real-time, version-specific documentation and code examples for AI programming assistants. It is directly integrated into prompts through the Model Context Protocol to solve the problem of LLMs using outdated information.
TypeScript
5.2K
4.7 points