Ejemplo de Servidor MCP Server

@LeoR22

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传输方式

stdio

作者

LeoR22

关于 Ejemplo de Servidor MCP Server

暂无概览

#mcp #model-context-protocol #server

Ejemplo de Servidor MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp_server_python": {
      "command": "uv",
      "args": [
        "init",
        "mcp-server"
      ]
    }
  }
}

Ejemplo de Servidor MCP Server 工具

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工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Ejemplo de Servidor MCP?

This repository contains an implementation of a Model Context Protocol (MCP) server. It demonstrates how to build a functional MCP server that can integrate with various large language model (LLM) clients.

How to use Ejemplo de Servidor MCP?

Install the uv package manager, initialize a project, add dependencies (mcp[cli] and httpx), and create a main.py server file. Run the server with uv run main.py. For Claude Desktop, configure its claude_desktop_config.json with the absolute path to the server.

Key features of Ejemplo de Servidor MCP

Implements the Model Context Protocol as a server
Exposes resources, tools, and prompts capabilities
Integrates with LLM clients like Claude Desktop
Uses Python 3.10+ with the MCP SDK 1.2.0+
Lightweight and scriptable via the uv package manager

Use cases of Ejemplo de Servidor MCP

Building custom AI assistants with access to local data
Connecting LLMs to local files, databases, or remote APIs
Prototyping MCP‑based integrations for development and testing

常见问题

What is MCP?

MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to LLMs, acting like a USB‑C port for AI apps.

What are the system requirements?

Python 3.10 or higher, MCP SDK 1.2.0 or higher, and the `uv` package manager.

How do I connect this server to Claude Desktop?

Edit `~/Library/Application Support/Claude/claude_desktop_config.json` with the absolute path to the server directory and the `uv run main.py` command, then restart Claude Desktop.

What capabilities can this server provide?

Resources (file‑like data), tools (functions callable by the LLM after user approval), and prompts (predefined templates for tasks).

What license is this project under?

This project is licensed under the MIT License.

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