Discover Awesome MCP Servers

MCP Gemini CLI

A server that allows interaction with Google's Gemini AI through the Gemini CLI tool using the Model Context Protocol, providing a standardized interface for querying Gemini with various options and configurations.

symbolics-mcp

An MCP server that enables LLMs to evaluate Lisp expressions on a Symbolics Genera machine over TCP. It bridges JSON-RPC requests to the Genera environment to return evaluation results, stdout, and stderr.

LinkedIn Ads MCP Server by CData

This project builds a read-only MCP server. For full read, write, update, delete, and action capabilities and a simplified setup, check out our free CData MCP Server for LinkedIn Ads (beta): https://www.cdata.com/download/download.aspx?sku=EBZK-V&type=beta

iRAG MCP Server

A Model Context Protocol server that enables Claude Desktop to generate images using Baidu's iRAG image generation API through a standardized interface.

The Movie DB MCP Server

A server that connects to The Movie Database API, enabling clients to fetch popular movies, now playing movies, movie details, cast information, and search for movies through a Message Control Protocol interface.

Gemini Email Subject Generator MCP

An MCP server that utilizes Google's Gemini Flash 2 model to generate AI-driven email subjects and detailed reasoning processes. It enables users to send emails via SMTP with dynamic content and save complex brainstorming sessions to local files.

Brave Search MCP Server

Integrates the Brave Search API to provide web and local search capabilities, including news, articles, and business information. It features smart fallbacks from local to web search and supports pagination and freshness controls.

Digital Brain MCP

A persistent long-term memory system that enables AI clients to store and recall notes, code, and research via semantic search. It utilizes Google Gemini embeddings and Supabase pgvector to provide a secure, searchable 'Second Brain' for MCP-compatible applications.

MCP Pyrefly

Integrates Pyrefly's real-time Python type checker with a gamification system that rewards fixing code errors with lollipops, using psychological manipulation to encourage LLMs to actively hunt for and fix all errors including imports.

Define-XML MCP Server

A TypeScript MCP server for interacting with CDISC Define-XML 2.0 and 2.1 files, enabling users to load, search, and retrieve clinical data metadata including datasets, variables, and codelists. It supports local file processing with document caching and provides responses in both JSON and Markdown formats.

gbox

Gru-sandbox (gbox) 是一个开源项目，它提供了一个可自托管的沙箱，用于 MCP 集成或其他 AI 代理用例。

🚀 deco.host — Instant MCP Server Deployment

开源 MCP 服务器平台。一个用于构建自定义 MCP 服务器并在任何地方部署的 Web Ninite。

Sharepoint MCP Server

通过 Microsoft Graph API 提供对组织 Sharepoint 文档的访问，从而使 AI 助手能够搜索和检索 Sharepoint 内容。

Reddit Scraper

Scrapes and queries Reddit posts, comments, and media from subreddits and user profiles without requiring API keys, storing data locally for offline access and filtering.

MCP Server & Client Test Environment

A testing environment for MCP server setup and client-server interactions, allowing users to verify basic MCP server functionality and test client-server communications including shell command execution.

FOFA Quake Hunter MCP Server

An MCP server that enables AI models to query FOFA, 360 Quake, and Hunter cyberspace mapping platforms for asset discovery and security research. It supports natural language parameter configuration and provides comprehensive search tools for retrieving IP, port, and domain data.

BotnBot MCP

MCP Server for Botnbot

GitHub Copilot Response Time Statistics MCP Server

Enables users to measure, track, and compare response times of GitHub Copilot and other AI models. Provides statistical analysis and performance comparison tools for evaluating AI model efficiency.

FastMCP Demo

A demonstration TypeScript MCP server that showcases basic MCP concepts with simple tools (greeting, calculator), text resources, and prompt templates for learning the Model Context Protocol.

Fetch JSONPath MCP

Enables efficient extraction of specific data from JSON APIs using JSONPath patterns, reducing token usage by up to 99% compared to fetching entire responses. Supports single and batch operations for both JSON extraction and raw text retrieval from URLs.

FL Studio MCP

An MCP server that connects Claude to FL Studio, allowing the AI to send melodies, chords, and drum patterns directly to the DAW via virtual MIDI ports.

MCP Imagen Server

Enables text-to-image generation, style transfer, background removal, and automatic image cropping using Google's Imagen AI models through the Model Context Protocol.

Zotero Chunk RAG

Enables passage-level semantic search over a Zotero library by extracting, chunking, and embedding PDF text using Gemini and ChromaDB. It provides MCP tools to perform topical searches and retrieve specific document passages with surrounding context.

Test MCP Server

A basic MCP server implementation for testing purposes. Communicates via stdio and is designed to work with MCP-compliant clients.

Google Classroom MCP

This MCP server provides comprehensive read and write access to Google Classroom, enabling management of courses, rosters, assignments, and grades. It supports full lifecycle operations for educational environments including student submissions, teacher invitations, and coursework organization.

FastMCP Production-Ready Server

A production-ready MCP server that enables users to interact with Neo4j databases through health checks and Cypher query tools. It features a structured, containerized architecture with built-in support for Azure deployments and environment-driven configuration.

Xcode MCP Server

A server that acts as a bridge between Claude and local Xcode projects, enabling AI-powered code assistance, project management, and automated development tasks without exposing your code to the internet.

Demo MCP Basic

好的，这是 MCP 服务器的 HTTP SSE 演示以及一个客户端： **服务器端 (Python - 使用 Flask):** ```python from flask import Flask, Response, request import time import json app = Flask(__name__) # 模拟 MCP 数据 def generate_mcp_data(): counter = 0 while True: data = { "timestamp": time.time(), "counter": counter, "message": f"MCP Data Update: {counter}" } yield f"data: {json.dumps(data)}\n\n" counter += 1 time.sleep(1) # 每秒更新一次 @app.route('/mcp_stream') def mcp_stream(): return Response(generate_mcp_data(), mimetype="text/event-stream") if __name__ == '__main__': app.run(debug=True, host='0.0.0.0', port=5000) ``` **代码解释:** * **`Flask`:** 使用 Flask 框架创建一个简单的 Web 服务器。 * **`generate_mcp_data()`:** 这是一个生成器函数，它模拟 MCP 数据。 * 它创建一个包含时间戳、计数器和消息的字典。 * 它使用 `yield` 关键字返回一个格式化为 SSE 事件的数据字符串。 `data: {json.dumps(data)}\n\n` 是 SSE 格式的关键。 `data:` 表示数据字段，`\n\n` 表示事件的结束。 * `time.sleep(1)` 模拟数据更新的间隔（这里是每秒一次）。 * **`/mcp_stream` 路由:** * `@app.route('/mcp_stream')` 定义了一个路由，当客户端访问 `/mcp_stream` 时，会调用 `mcp_stream()` 函数。 * `Response(generate_mcp_data(), mimetype="text/event-stream")` 创建一个 HTTP 响应，其中包含 `generate_mcp_data()` 生成的数据流，并将 `mimetype` 设置为 `text/event-stream`。 这是告诉客户端这是一个 SSE 流的关键。 * **`app.run(...)`:** 启动 Flask 服务器。 `host='0.0.0.0'` 允许从任何 IP 地址访问服务器。 **客户端 (JavaScript - HTML):** ```html <!DOCTYPE html> <html> <head> <title>MCP SSE Client</title> </head> <body> <h1>MCP Data Stream</h1> <div id="mcp-data"></div> <script> const eventSource = new EventSource('/mcp_stream'); // 替换为你的服务器地址 eventSource.onmessage = (event) => { const data = JSON.parse(event.data); const mcpDataElement = document.getElementById('mcp-data'); mcpDataElement.innerHTML += `<p>${JSON.stringify(data)}</p>`; }; eventSource.onerror = (error) => { console.error("EventSource failed:", error); eventSource.close(); // 关闭连接，防止无限重试 }; </script> </body> </html> ``` **代码解释:** * **`EventSource`:** `new EventSource('/mcp_stream')` 创建一个 `EventSource` 对象，连接到服务器的 `/mcp_stream` 端点。 确保将 `/mcp_stream` 替换为你的服务器地址（例如 `http://localhost:5000/mcp_stream`）。 * **`onmessage` 事件处理程序:** * `eventSource.onmessage = (event) => { ... }` 定义了一个事件处理程序，当服务器发送新数据时，该处理程序会被调用。 * `event.data` 包含服务器发送的数据。 * `JSON.parse(event.data)` 将 JSON 字符串解析为 JavaScript 对象。 * `document.getElementById('mcp-data')` 获取 HTML 中 `id` 为 `mcp-data` 的元素。 * `mcpDataElement.innerHTML += `<p>${JSON.stringify(data)}</p>`;` 将接收到的数据添加到 HTML 元素中。 * **`onerror` 事件处理程序:** * `eventSource.onerror = (error) => { ... }` 定义了一个事件处理程序，当发生错误时，该处理程序会被调用。 * `console.error("EventSource failed:", error);` 将错误信息输出到控制台。 * `eventSource.close();` 关闭 `EventSource` 连接，防止客户端无限重试连接。 **如何运行:** 1. **保存文件:** 将 Python 代码保存为 `mcp_server.py`，将 HTML 代码保存为 `mcp_client.html`。 2. **安装 Flask:** 在命令行中运行 `pip install flask`。 3. **运行服务器:** 在命令行中运行 `python mcp_server.py`。 4. **打开客户端:** 在浏览器中打开 `mcp_client.html`。 **预期结果:** 你将在浏览器中看到一个标题为 "MCP Data Stream" 的页面，并且页面会不断更新，显示来自服务器的 MCP 数据。 每次服务器发送新数据时，都会在页面上添加一个新的 `<p>` 元素，显示 JSON 格式的数据。 **重要注意事项:** * **CORS (跨域资源共享):** 如果你的客户端和服务器运行在不同的域名或端口上，你可能需要配置 CORS。 你可以使用 Flask 的 `flask_cors` 扩展来处理 CORS。 例如： ```python from flask import Flask, Response from flask_cors import CORS import time import json app = Flask(__name__) CORS(app) # 允许所有来源的跨域请求 # ... (其余代码不变) ``` 然后运行 `pip install flask_cors`。 * **错误处理:** 在实际应用中，你需要更完善的错误处理机制，例如处理连接错误、数据解析错误等。 * **数据格式:** 根据你的实际需求调整 MCP 数据的格式。 * **服务器地址:** 确保客户端中的 `EventSource` 连接到正确的服务器地址。 这个演示提供了一个基本的 MCP 服务器和客户端的框架。你可以根据你的具体需求进行修改和扩展。 希望这个例子能帮助你理解如何使用 HTTP SSE 实现 MCP 服务器。

MCP Stock Analysis Server

通过 Yahoo Finance API 提供对实时和历史印度股票数据的访问，使本地 LLM 能够通过 MCP 兼容的代理（如 Claude Desktop 和 Cursor）检索股票信息。

Food Tracker MCP Server

Enables tracking food intake and nutrition using the USDA FoodData Central database. Supports logging meals, setting daily nutrition goals, viewing food diaries, and analyzing nutrition trends over time with local SQLite storage.