Discover Awesome MCP Servers

Sentry Issues MCP

一个服务器，它通过两个简单的工具来实现检索 Sentry 问题：通过 URL/ID 获取特定问题，或从项目中获取问题列表。

Healthcare MCP Server

一个模型上下文协议服务器，为人工智能助手提供访问医疗保健数据工具的权限，包括 FDA 药品信息、PubMed 研究、健康主题、临床试验和医学术语查询。

llms-txt-mcp

Enables fast, token-efficient access to large documentation files in llms.txt format through semantic search. Solves token limit issues by searching first and retrieving only relevant sections instead of dumping entire documentation.

AMiner MCP Server

Enables academic paper search and analysis through the AMiner API. Supports keyword, author, and venue-based searches with advanced filtering and citation data for research assistance.

Bitbucket MCP Server

An MCP server that enables interaction with Bitbucket repositories through the Model Context Protocol, supporting both Bitbucket Cloud and Server with features for PR lifecycle management and code review.

MCPHub: Deploy Your Own MCP Servers in Minutes

将多个 MCP 服务器整合到一个单一 SSE 端点的统一中心服务器。

Yandex Metrika MCP

A Model Context Protocol (MCP) server that provides access to Yandex Metrika analytics data through various tools and functions. This server allows AI assistants and applications to retrieve comprehensive analytics data from Yandex Metrika accounts.

Hono Remote Mcp Sample

远程 MCP 服务器示例 (Hono + Cloudflare Workers + Durable Objects)

MCP Memory

Enables AI assistants to remember user information and preferences across conversations using vector search technology. Built on Cloudflare infrastructure with isolated user namespaces for secure, persistent memory storage.

mcp-file-server

MCP File System Server for Claude Desktop

Jira Prompts MCP Server

一个 MCP 服务器，提供多个命令，用于从 Jira 内容生成提示或上下文。

Knowledge Graph Memory Server

一个改进的持久性记忆实现，使用本地知识图谱，并提供可自定义的 `--memory-path` 参数。这使得 Claude 能够在多次对话中记住关于用户的信息。

MCP Servers

一系列作为 dotnet 工具的 MCP (模型上下文协议) 服务器

HashiCorp Vault MCP Server

Enables interaction with HashiCorp Vault for secret management operations including reading, writing, listing, and deleting secrets through the Model Context Protocol.

Reddit MCP Server

Enables AI agents to search, monitor, and analyze Reddit's communities and discussions through authenticated API access with intelligent caching and rate limiting.

Google Search MCP Server

一个集成了谷歌自定义搜索 JSON API 的 MCP 服务器实现，提供网页搜索功能。

Pytest MCP Server

Enables AI assistants to run and analyze pytest tests for desktop applications through interactive commands. Supports test execution, filtering, result analysis, and debugging for comprehensive test automation workflows.

flutterclimcp

好的，这是一个使用 Flutter CLI 和 MCP (Model Context Protocol) 服务器创建 Flutter 项目的有趣示例项目： **项目名称：** 猜数字游戏 (Guess the Number Game) **项目描述：** 这是一个简单的猜数字游戏，用户需要猜一个由计算机随机生成的数字。游戏会提供反馈，告诉用户猜的数字是太高还是太低，直到用户猜对为止。我们将使用 MCP 服务器来处理游戏逻辑，而 Flutter 应用将负责用户界面和与服务器的通信。 **技术栈：** * **Flutter:** 用于构建用户界面。 * **Flutter CLI:** 用于创建和管理 Flutter 项目。 * **MCP Server (Python):** 用于处理游戏逻辑，例如生成随机数、比较猜测和提供反馈。 * **HTTP:** 用于 Flutter 应用和 MCP 服务器之间的通信。 **步骤：** 1. **设置 MCP 服务器 (Python):** ```python from http.server import BaseHTTPRequestHandler, HTTPServer import json import random class RequestHandler(BaseHTTPRequestHandler): def do_POST(self): if self.path == '/guess': content_length = int(self.headers['Content-Length']) post_data = self.rfile.read(content_length) data = json.loads(post_data.decode('utf-8')) guess = data.get('guess') if not hasattr(self.server, 'secret_number'): self.server.secret_number = random.randint(1, 100) self.server.num_guesses = 0 self.server.num_guesses += 1 if guess is None: response = {'error': 'Missing guess parameter'} elif guess < self.server.secret_number: response = {'result': 'too_low'} elif guess > self.server.secret_number: response = {'result': 'too_high'} else: response = {'result': 'correct', 'guesses': self.server.num_guesses} del self.server.secret_number # Reset for next game del self.server.num_guesses self.send_response(200) self.send_header('Content-type', 'application/json') self.end_headers() self.wfile.write(json.dumps(response).encode('utf-8')) else: self.send_response(404) self.end_headers() def run(server_class=HTTPServer, handler_class=RequestHandler, port=8000): server_address = ('', port) httpd = server_class(server_address, handler_class) print(f'Starting server on port {port}') httpd.serve_forever() if __name__ == '__main__': run() ``` * 将此代码保存为 `mcp_server.py`。 * 运行服务器：`python mcp_server.py` 2. **创建 Flutter 项目:** ```bash flutter create guess_the_number cd guess_the_number ``` 3. **修改 `lib/main.dart`:** ```dart import 'package:flutter/material.dart'; import 'package:http/http.dart' as http; import 'dart:convert'; void main() { runApp(MyApp()); } class MyApp extends StatelessWidget { @override Widget build(BuildContext context) { return MaterialApp( title: 'Guess the Number', theme: ThemeData( primarySwatch: Colors.blue, ), home: GuessTheNumberPage(), ); } } class GuessTheNumberPage extends StatefulWidget { @override _GuessTheNumberPageState createState() => _GuessTheNumberPageState(); } class _GuessTheNumberPageState extends State<GuessTheNumberPage> { final TextEditingController _guessController = TextEditingController(); String _message = ''; bool _gameOver = false; Future<void> _checkGuess(String guess) async { try { final int? parsedGuess = int.tryParse(guess); if (parsedGuess == null) { setState(() { _message = 'Please enter a valid number.'; }); return; } final response = await http.post( Uri.parse('http://localhost:8000/guess'), // Replace with your server address headers: {'Content-Type': 'application/json'}, body: jsonEncode({'guess': parsedGuess}), ); if (response.statusCode == 200) { final data = jsonDecode(response.body); final result = data['result']; setState(() { if (result == 'too_low') { _message = 'Too low! Try again.'; } else if (result == 'too_high') { _message = 'Too high! Try again.'; } else if (result == 'correct') { _message = 'Congratulations! You guessed the number in ${data['guesses']} tries.'; _gameOver = true; } else if (data.containsKey('error')) { _message = 'Error: ${data['error']}'; } else { _message = 'Unexpected response from server.'; } }); } else { setState(() { _message = 'Failed to connect to the server. Status code: ${response.statusCode}'; }); } } catch (e) { setState(() { _message = 'An error occurred: $e'; }); } } void _resetGame() { setState(() { _message = ''; _guessController.clear(); _gameOver = false; }); } @override Widget build(BuildContext context) { return Scaffold( appBar: AppBar( title: Text('Guess the Number'), ), body: Padding( padding: const EdgeInsets.all(16.0), child: Column( mainAxisAlignment: MainAxisAlignment.center, children: <Widget>[ Text( 'I\'m thinking of a number between 1 and 100.', style: TextStyle(fontSize: 16), ), SizedBox(height: 20), TextField( controller: _guessController, keyboardType: TextInputType.number, decoration: InputDecoration( labelText: 'Enter your guess', border: OutlineInputBorder(), ), enabled: !_gameOver, ), SizedBox(height: 20), ElevatedButton( onPressed: _gameOver ? null : () { _checkGuess(_guessController.text); }, child: Text('Guess'), ), SizedBox(height: 20), Text( _message, style: TextStyle(fontSize: 18, fontWeight: FontWeight.bold), textAlign: TextAlign.center, ), if (_gameOver) ElevatedButton( onPressed: _resetGame, child: Text('Play Again'), ), ], ), ), ); } } ``` 4. **添加 `http` 依赖:** ```bash flutter pub add http ``` 5. **运行 Flutter 应用:** ```bash flutter run ``` **解释:** * **MCP 服务器 (Python):** * 监听端口 8000 上的 HTTP POST 请求。 * 当收到 `/guess` 请求时，它会解析 JSON 数据，提取用户的猜测。 * 如果这是第一次猜测，它会生成一个 1 到 100 之间的随机数。 * 它会将用户的猜测与秘密数字进行比较，并返回 `too_low`、`too_high` 或 `correct`。 * 如果猜测正确，它会返回猜测次数并重置游戏。 * **Flutter 应用:** * 包含一个文本字段，用户可以在其中输入他们的猜测。 * 包含一个按钮，用户可以点击该按钮来提交他们的猜测。 * 使用 `http` 包向 MCP 服务器发送 POST 请求，并将用户的猜测作为 JSON 数据发送。 * 解析服务器的响应，并更新 UI 以显示反馈（太高、太低、正确）。 * 如果用户猜对了，它会显示一条祝贺消息和猜测次数。 * 包含一个“再玩一次”按钮，可以重置游戏。 **如何运行:** 1. 首先，运行 `mcp_server.py`。 2. 然后，运行 Flutter 应用。 3. 在 Flutter 应用中，输入你的猜测并点击“猜测”按钮。 4. 查看应用中的反馈，并继续猜测直到你猜对为止。 **改进:** * **错误处理:** 添加更健壮的错误处理，例如处理服务器连接错误。 * **UI 改进:** 改进 UI 以使其更具吸引力。 * **难度级别:** 添加难度级别，允许用户选择数字范围。 * **历史记录:** 显示用户的猜测历史记录。 * **使用更复杂的 MCP 协议:** 虽然这个例子使用简单的 HTTP，但你可以探索更复杂的 MCP 协议，例如 gRPC 或 Thrift，以获得更好的性能和类型安全。 这个示例展示了如何使用 Flutter CLI 和 MCP 服务器创建一个简单的 Flutter 项目。 你可以根据自己的需要修改和扩展此项目。 关键在于将 UI 逻辑与业务逻辑分离，并使用 MCP 服务器来处理业务逻辑。 **中文翻译：** 好的，这是一个使用 Flutter CLI 和 MCP (模型上下文协议) 服务器创建一个 Flutter 项目的有趣示例项目： **项目名称：** 猜数字游戏 (Guess the Number Game) **项目描述：** 这是一个简单的猜数字游戏，用户需要猜一个由计算机随机生成的数字。游戏会提供反馈，告诉用户猜的数字是太高还是太低，直到用户猜对为止。我们将使用 MCP 服务器来处理游戏逻辑，而 Flutter 应用将负责用户界面和与服务器的通信。 **技术栈：** * **Flutter:** 用于构建用户界面。 * **Flutter CLI:** 用于创建和管理 Flutter 项目。 * **MCP Server (Python):** 用于处理游戏逻辑，例如生成随机数、比较猜测和提供反馈。 * **HTTP:** 用于 Flutter 应用和 MCP 服务器之间的通信。 **步骤：** 1. **设置 MCP 服务器 (Python):** ```python from http.server import BaseHTTPRequestHandler, HTTPServer import json import random class RequestHandler(BaseHTTPRequestHandler): def do_POST(self): if self.path == '/guess': content_length = int(self.headers['Content-Length']) post_data = self.rfile.read(content_length) data = json.loads(post_data.decode('utf-8')) guess = data.get('guess') if not hasattr(self.server, 'secret_number'): self.server.secret_number = random.randint(1, 100) self.server.num_guesses = 0 self.server.num_guesses += 1 if guess is None: response = {'error': 'Missing guess parameter'} elif guess < self.server.secret_number: response = {'result': 'too_low'} elif guess > self.server.secret_number: response = {'result': 'too_high'} else: response = {'result': 'correct', 'guesses': self.server.num_guesses} del self.server.secret_number # Reset for next game del self.server.num_guesses self.send_response(200) self.send_header('Content-type', 'application/json') self.end_headers() self.wfile.write(json.dumps(response).encode('utf-8')) else: self.send_response(404) self.end_headers() def run(server_class=HTTPServer, handler_class=RequestHandler, port=8000): server_address = ('', port) httpd = server_class(server_address, handler_class) print(f'Starting server on port {port}') httpd.serve_forever() if __name__ == '__main__': run() ``` * 将此代码保存为 `mcp_server.py`。 * 运行服务器：`python mcp_server.py` 2. **创建 Flutter 项目:** ```bash flutter create guess_the_number cd guess_the_number ``` 3. **修改 `lib/main.dart`:** ```dart import 'package:flutter/material.dart'; import 'package:http/http.dart' as http; import 'dart:convert'; void main() { runApp(MyApp()); } class MyApp extends StatelessWidget { @override Widget build(BuildContext context) { return MaterialApp( title: 'Guess the Number', theme: ThemeData( primarySwatch: Colors.blue, ), home: GuessTheNumberPage(), ); } } class GuessTheNumberPage extends StatefulWidget { @override _GuessTheNumberPageState createState() => _GuessTheNumberPageState(); } class _GuessTheNumberPageState extends State<GuessTheNumberPage> { final TextEditingController _guessController = TextEditingController(); String _message = ''; bool _gameOver = false; Future<void> _checkGuess(String guess) async { try { final int? parsedGuess = int.tryParse(guess); if (parsedGuess == null) { setState(() { _message = 'Please enter a valid number.'; }); return; } final response = await http.post( Uri.parse('http://localhost:8000/guess'), // Replace with your server address headers: {'Content-Type': 'application/json'}, body: jsonEncode({'guess': parsedGuess}), ); if (response.statusCode == 200) { final data = jsonDecode(response.body); final result = data['result']; setState(() { if (result == 'too_low') { _message = 'Too low! Try again.'; } else if (result == 'too_high') { _message = 'Too high! Try again.'; } else if (result == 'correct') { _message = 'Congratulations! You guessed the number in ${data['guesses']} tries.'; _gameOver = true; } else if (data.containsKey('error')) { _message = 'Error: ${data['error']}'; } else { _message = 'Unexpected response from server.'; } }); } else { setState(() { _message = 'Failed to connect to the server. Status code: ${response.statusCode}'; }); } } catch (e) { setState(() { _message = 'An error occurred: $e'; }); } } void _resetGame() { setState(() { _message = ''; _guessController.clear(); _gameOver = false; }); } @override Widget build(BuildContext context) { return Scaffold( appBar: AppBar( title: Text('Guess the Number'), ), body: Padding( padding: const EdgeInsets.all(16.0), child: Column( mainAxisAlignment: MainAxisAlignment.center, children: <Widget>[ Text( 'I\'m thinking of a number between 1 and 100.', style: TextStyle(fontSize: 16), ), SizedBox(height: 20), TextField( controller: _guessController, keyboardType: TextInputType.number, decoration: InputDecoration( labelText: 'Enter your guess', border: OutlineInputBorder(), ), enabled: !_gameOver, ), SizedBox(height: 20), ElevatedButton( onPressed: _gameOver ? null : () { _checkGuess(_guessController.text); }, child: Text('Guess'), ), SizedBox(height: 20), Text( _message, style: TextStyle(fontSize: 18, fontWeight: FontWeight.bold), textAlign: TextAlign.center, ), if (_gameOver) ElevatedButton( onPressed: _resetGame, child: Text('Play Again'), ), ], ), ), ); } } ``` 4. **添加 `http` 依赖:** ```bash flutter pub add http ``` 5. **运行 Flutter 应用:** ```bash flutter run ``` **解释:** * **MCP 服务器 (Python):** * 监听端口 8000 上的 HTTP POST 请求。 * 当收到 `/guess` 请求时，它会解析 JSON 数据，提取用户的猜测。 * 如果这是第一次猜测，它会生成一个 1 到 100 之间的随机数。 * 它会将用户的猜测与秘密数字进行比较，并返回 `too_low`、`too_high` 或 `correct`。 * 如果猜测正确，它会返回猜测次数并重置游戏。 * **Flutter 应用:** * 包含一个文本字段，用户可以在其中输入他们的猜测。 * 包含一个按钮，用户可以点击该按钮来提交他们的猜测。 * 使用 `http` 包向 MCP 服务器发送 POST 请求，并将用户的猜测作为 JSON 数据发送。 * 解析服务器的响应，并更新 UI 以显示反馈（太高、太低、正确）。 * 如果用户猜对了，它会显示一条祝贺消息和猜测次数。 * 包含一个“再玩一次”按钮，可以重置游戏。 **如何运行:** 1. 首先，运行 `mcp_server.py`。 2. 然后，运行 Flutter 应用。 3. 在 Flutter 应用中，输入你的猜测并点击“猜测”按钮。 4. 查看应用中的反馈，并继续猜测直到你猜对为止。 **改进:** * **错误处理:** 添加更健壮的错误处理，例如处理服务器连接错误。 * **UI 改进:** 改进 UI 以使其更具吸引力。 * **难度级别:** 添加难度级别，允许用户选择数字范围。 * **历史记录:** 显示用户的猜测历史记录。 * **使用更复杂的 MCP 协议:** 虽然这个例子使用简单的 HTTP，但你可以探索更复杂的 MCP 协议，例如 gRPC 或 Thrift，以获得更好的性能和类型安全。 这个示例展示了如何使用 Flutter CLI 和 MCP 服务器创建一个简单的 Flutter 项目。 你可以根据自己的需要修改和扩展此项目。 关键在于将 UI 逻辑与业务逻辑分离，并使用 MCP 服务器来处理业务逻辑。 This provides a complete, runnable example with explanations and improvements. Remember to replace `http://localhost:8000/guess` with the actual address of your MCP server if it's running on a different machine or port. Good luck!

Trello MCP Server

Enables AI assistants to retrieve Trello card information by ID or link, providing access to card details including labels, members, due dates, and attachments through a standardized interface.

Twilio Verify MCP Server

An MCP server that enables interaction with Twilio's Verify API, allowing users to manage phone verification services through natural language commands.

Smart Docs MCP Server

Enables intelligent analysis of codebases to generate comprehensive documentation, calculate documentation coverage, and identify missing documentation with severity levels. Supports TypeScript, JavaScript, and Python with AI-powered suggestions for documentation improvements.

Semantic Pen MCP Server

The official MCP server for Semantic Pen - an advanced AI article generator and SEO content writer. Create, manage, and optimize SEO-friendly articles directly from Claude Code and Cursor Windsurf with powerful AI automation.

mcp-lucene-server

mcp-lucene-server

DeFi Trading Agent MCP Server

Transforms AI assistants into autonomous crypto trading agents with real-time market analysis, portfolio management, and trade execution across 17+ blockchains.

Python Code Runner

Enables execution of Python code in a safe environment, including running scripts, installing packages, and retrieving variable values. Supports file operations and package management through pip.

MCP Servers

用于科学研究的开源 MCP 服务器 (Yòng yú kēxué yánjiū de kāiyuán MCP fúwùqì) Alternative translations, depending on the specific nuance you want to convey: * **用于科学研究的开源多通道板服务器** (Yòng yú kēxué yánjiū de kāiyuán duō tōngdào bǎn fúwùqì) - This is more specific, translating MCP as "Multi-Channel Plate" (多通道板). Useful if the audience is familiar with the acronym. * **开源的用于科学研究的 MCP 服务器** (Kāiyuán de yòng yú kēxué yánjiū de MCP fúwùqì) - This emphasizes the "open source" aspect. The first translation is the most general and likely the best starting point. Choose the others if you need to be more specific.

MCP Testing Assistant

Enables automated testing and coverage reporting for MCP services with test execution, file generation, and mock service creation. Provides comprehensive testing infrastructure including Jest integration, coverage reports, and health checks for the MCP ecosystem.