发现优秀的 MCP 服务器

LinkedIn Custom MCP Server

Enables AI agents to manage professional networking on LinkedIn by providing tools for posting updates, searching for jobs, and analyzing profiles. It facilitates secure interaction with the LinkedIn platform through OAuth 2.0 authentication and the Model Context Protocol.

YARR Media Stack MCP Server

一个综合的模型上下文协议服务器，连接大型语言模型（LLM）与自托管媒体服务，从而实现对电视节目、电影、下载和通知的自然语言控制，同时保持传统的 API 访问方式。

YouTube MCP Server

一个服务器，它通过模型上下文协议实现与 YouTube 数据的交互，允许用户搜索视频、检索视频/频道的详细信息以及获取评论。

Semantic Scholar MCP Server

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MCP Demo

Okay, I can't directly "demonstrate an MCP server" in the sense of running code and showing you output here. That requires a real server environment and access to aviation weather data APIs. However, I can provide you with a conceptual outline and code snippets (in Python, a common language for this) to illustrate how such a server *could* be built. This will give you a solid understanding of the components involved. **Conceptual Outline** 1. **Data Source:** The server needs to fetch aviation weather data from a reliable source. Common sources include: * **NOAA Aviation Weather Center (AWC):** Provides METARs, TAFs, PIREPs, and other aviation weather products. Often accessed via their XML/text feeds. * **Aviation Weather APIs (Commercial):** Some companies offer paid APIs that provide more structured data and potentially better performance. Examples include Aviation Edge, CheckWX, etc. 2. **Server Framework:** Choose a web server framework to handle incoming requests and send responses. Popular choices include: * **Flask (Python):** Lightweight and easy to learn. Good for simple APIs. * **FastAPI (Python):** Modern, high-performance, and automatically generates API documentation. * **Node.js (JavaScript):** If you prefer JavaScript. Express.js is a common framework. 3. **Data Parsing and Storage (Optional):** * **Parsing:** The data from the source (e.g., XML from NOAA) needs to be parsed into a usable format (e.g., Python dictionaries or objects). * **Storage (Optional):** For performance, you might want to cache the weather data in a database (e.g., Redis, PostgreSQL) or in memory. This avoids hitting the external API too frequently. 4. **API Endpoints:** Define the API endpoints that clients will use to request data. For example: * `/metar/{icao}`: Get the METAR for a specific airport (ICAO code). * `/taf/{icao}`: Get the TAF for a specific airport. * `/airports/search?q={query}`: Search for airports by name or ICAO code. 5. **Error Handling:** Implement proper error handling to gracefully handle issues like invalid airport codes, API errors, and network problems. 6. **Security (Important):** If the server is publicly accessible, implement security measures to prevent abuse. This might include rate limiting, authentication, and authorization. **Python (Flask) Example Code Snippets** ```python from flask import Flask, jsonify, request import requests import xml.etree.ElementTree as ET # For parsing XML (if using NOAA) app = Flask(__name__) # Replace with your actual NOAA ADDS URL or other API endpoint NOAA_ADDS_URL = "https://aviationweather.gov/adds/dataserver/.......your_query_here......." # Example, needs a real query # In-memory cache (for demonstration purposes only; use a real database for production) weather_cache = {} def fetch_metar_from_noaa(icao): """Fetches METAR data from NOAA ADDS for a given ICAO code.""" try: # Construct the NOAA ADDS query (example, adjust as needed) query = f"?dataSource=metars&requestType=retrieve&format=xml&stationString={icao}&hoursBeforeNow=1" url = NOAA_ADDS_URL.replace(".......your_query_here.......", query) response = requests.get(url) response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx) xml_data = response.text root = ET.fromstring(xml_data) # Parse the XML to extract the METAR information metar_element = root.find(".//METAR/raw_text") # Adjust path based on XML structure if metar_element is not None: metar_text = metar_element.text return metar_text else: return None # METAR not found except requests.exceptions.RequestException as e: print(f"Error fetching data from NOAA: {e}") return None except ET.ParseError as e: print(f"Error parsing XML: {e}") return None @app.route('/metar/<icao>') def get_metar(icao): """API endpoint to get METAR data for a given ICAO code.""" icao = icao.upper() # Ensure ICAO is uppercase # Check the cache first if icao in weather_cache: print(f"Fetching METAR for {icao} from cache") metar = weather_cache[icao] else: print(f"Fetching METAR for {icao} from NOAA") metar = fetch_metar_from_noaa(icao) if metar: weather_cache[icao] = metar # Store in cache if metar: return jsonify({'icao': icao, 'metar': metar}) else: return jsonify({'error': 'METAR not found for this ICAO code'}), 404 @app.route('/airports/search') def search_airports(): """Example endpoint for searching airports (replace with real implementation).""" query = request.args.get('q') if not query: return jsonify({'error': 'Missing query parameter'}), 400 # Replace this with a real airport search implementation (e.g., from a database) # This is just a placeholder if query.lower() == "jfk": results = [{"icao": "KJFK", "name": "John F. Kennedy International Airport"}] elif query.lower() == "lax": results = [{"icao": "KLAX", "name": "Los Angeles International Airport"}] else: results = [] return jsonify(results) if __name__ == '__main__': # Production: Use a proper WSGI server (e.g., gunicorn, uWSGI) app.run(debug=True) # Debug mode for development only ``` **Explanation of the Code:** * **Imports:** Imports necessary libraries (Flask, `requests` for making HTTP requests, `xml.etree.ElementTree` for parsing XML). * **`NOAA_ADDS_URL`:** **CRITICAL:** You *must* replace this with a valid URL for the NOAA ADDS server. The example is just a placeholder. You'll need to construct the correct query parameters to get the data you want. Refer to the NOAA ADDS documentation. * **`weather_cache`:** A simple in-memory dictionary to cache weather data. This is for demonstration only. In a real application, use a database like Redis. * **`fetch_metar_from_noaa(icao)`:** * Constructs the NOAA ADDS URL with the ICAO code. * Uses `requests` to fetch the data. * Parses the XML response using `xml.etree.ElementTree`. * Extracts the METAR text from the XML. **Important:** The XML structure can be complex. You'll need to carefully inspect the NOAA ADDS XML response to determine the correct XPath to the METAR data. * Handles potential errors (network errors, XML parsing errors). * **`get_metar(icao)`:** * The API endpoint for `/metar/{icao}`. * Checks the cache first. * If not in the cache, fetches the data from NOAA. * Returns the METAR data as a JSON response. * Returns a 404 error if the METAR is not found. * **`search_airports()`:** A placeholder for an airport search endpoint. You'll need to replace this with a real implementation that queries a database of airports. * **`app.run(debug=True)`:** Starts the Flask development server. **Important:** Do not use `debug=True` in a production environment. Use a proper WSGI server (e.g., gunicorn, uWSGI). **To Run This Example (After Replacing the Placeholder URL):** 1. **Install Flask and Requests:** ```bash pip install flask requests ``` 2. **Save the code:** Save the code as a Python file (e.g., `aviation_server.py`). 3. **Run the server:** ```bash python aviation_server.py ``` 4. **Test the API:** Open a web browser or use `curl` to test the API endpoints: * `http://127.0.0.1:5000/metar/KJFK` (Replace `KJFK` with a valid ICAO code) * `http://127.0.0.1:5000/airports/search?q=jfk` **Key Improvements and Considerations for a Production System:** * **Error Handling:** More robust error handling, including logging and more informative error messages. * **Configuration:** Use environment variables or a configuration file to store API keys, database connection strings, and other settings. * **Data Validation:** Validate the ICAO code and other input parameters to prevent errors and security vulnerabilities. * **Rate Limiting:** Implement rate limiting to prevent abuse of the API. * **Authentication/Authorization:** If the API is sensitive, implement authentication and authorization to control access. * **Asynchronous Operations:** For better performance, use asynchronous operations (e.g., `asyncio` in Python) to fetch data from the external API without blocking the server. * **Testing:** Write unit tests and integration tests to ensure the server is working correctly. * **Deployment:** Deploy the server to a production environment (e.g., AWS, Google Cloud, Azure) using a proper WSGI server (e.g., gunicorn, uWSGI). * **Monitoring:** Monitor the server's performance and error rates. * **TAF Support:** Implement the `/taf/{icao}` endpoint to fetch Terminal Aerodrome Forecasts. This will involve a similar process of querying the NOAA ADDS server (or another API) and parsing the TAF data. * **Data Source Abstraction:** Create an abstraction layer for the data source. This will make it easier to switch to a different API in the future. **Chinese Translation of Key Concepts** * **MCP Server:** MCP服务器 (MCP fúwùqì) - While "MCP" isn't a standard term in this context, it's understood as a server providing specific data. A more descriptive term might be 航空气象数据服务器 (Hángkōng qìxiàng shùjù fúwùqì) - Aviation Weather Data Server. * **Aviation Weather Data:** 航空气象数据 (Hángkōng qìxiàng shùjù) * **METAR:** 机场气象报告 (Jīchǎng qìxiàng bàogào) * **TAF:** 机场预报 (Jīchǎng yùbào) * **ICAO Code:** 国际民航组织机场代码 (Guójì Mínháng Zǔzhī jīchǎng dàimǎ) * **API Endpoint:** API端点 (API duāndiǎn) * **Data Source:** 数据源 (Shùjù yuán) * **Parsing:** 解析 (Jiěxī) * **Caching:** 缓存 (Huǎncún) * **Error Handling:** 错误处理 (Cuòwù chǔlǐ) * **Rate Limiting:** 速率限制 (Sùlǜ xiànzhì) * **Authentication:** 身份验证 (Shēnfèn yànzhèng) * **Authorization:** 授权 (Shòuquán) This comprehensive explanation and code example should give you a strong foundation for building your own aviation weather data server. Remember to replace the placeholder URL with a valid NOAA ADDS query and adapt the code to your specific needs. Good luck!

Karma MCP Server

Enables Claude to interact with Karma Alert dashboard to monitor and analyze Kubernetes alerts. Provides tools to check alert status, filter by cluster/severity, get detailed alert information, and analyze alert statistics and trends.

Brain MCP Server

Enables interaction with Brain service for thought management, including saving thoughts with metadata, quick capture with auto-classification, searching, and retrieving entries through natural language.

Synergy/DE MCP Server

A read-only server that exposes Synergy/DE documentation as tools and resources for searching, retrieving, and browsing topics. It features full-text search, version support, and optimized content chunking for seamless integration with LLMs and MCP clients.

Alpaca MCP Gold Standard

A comprehensive MCP server for professional trading operations with Alpaca, providing 31 tools for account management, market data, order execution, custom strategy execution, and advanced portfolio analytics with intelligent position classification.

Hacker News MCP Server

为 LLM 客户端添加强大的 Hacker News 集成，允许用户通过模型上下文协议访问故事、评论、用户个人资料和搜索功能。

mcp-for-apache-ofbiz

A proof of concept MCP server for Apache OFBiz

better-auth-mcp-server MCP Server

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Mastercard BIN Table MCP Server

An MCP server that provides access to Mastercard's BIN Table Resource API, allowing users to look up and interact with Bank Identification Number data through natural language queries.

repo-to-txt-mcp

用于分析和转换 Git 仓库为文本文件，以供 LLM 上下文使用的 MCP 服务器。 (Alternatively, a more literal translation could be:) 用于分析和将 Git 仓库转换为文本文件，以供 LLM 上下文使用的 MCP 服务器。 **Explanation of Choices:** * **MCP Server:** This is kept as "MCP 服务器" as it's likely a specific product or technology with a known abbreviation. If you have more context about what MCP stands for, it might be possible to translate it more fully. * **Analyzing and Converting:** "分析和转换" is a standard and clear translation for these actions. * **Git Repositories:** "Git 仓库" is the standard Chinese term for Git repositories. * **Text Files:** "文本文件" is the standard Chinese term for text files. * **LLM Context:** "LLM 上下文" is used. While you could translate "LLM" to "大型语言模型" (Large Language Model), keeping it as "LLM" is common in technical contexts, especially if the target audience is familiar with the abbreviation. "上下文" is the standard translation for "context." * **For:** "以供" is a more formal and precise way to say "for" in this context, implying "for the purpose of." The first translation is slightly more natural-sounding in Chinese. The second is more literal. Choose the one that best suits your needs and target audience.

mcp-server-restart

镜子 (jìng zi)

SuperiorAPIs MCP Server Tool

一个基于 Python 的 MCP 服务器，可以从 SuperiorAPIs 动态获取插件定义，并基于 OpenAPI 模式自动生成工具函数，从而实现与 API 服务的无缝集成。

Delve MCP

一个基于 TypeScript 的 MCP 服务器，为 Go 程序的 Delve 调试器提供完整的接口，从而可以通过自然语言命令调试、追踪和分析 Go 代码。

Todo List MCP Server

一个模型上下文协议 (MCP) 服务器，提供用于管理待办事项的工具，包括创建、更新、完成、删除、搜索和总结任务。

WebSearch MCP Server

A server that enables web searches using different providers (currently Bing) and converts URL content to Markdown, with support for retrieving WeChat official account articles.

iMessage Max

Enables AI assistants to read, search, and send iMessages with features like contact name resolution, session grouping, and attachment listing. It provides intent-aligned tools to efficiently navigate conversation history and manage messages through natural language queries.

Swagger MCP

一个 MCP 服务器，它可以连接到 Swagger 规范，并帮助 AI 构建所有必需的模型，从而为该服务生成一个 MCP 服务器。

HANA Cloud MCP Server

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MCPHy

Transform REST APIs into intelligent, chat-driven MCP servers with zero code changes. Simply point it at your Swagger/OpenAPI specification to get natural language querying capabilities powered by AI.

Multi-Agent Communication Platform (MCP)

Enables multiple Claude Code instances to collaborate in real-time through channels, allowing AI agents to work together on projects without requiring local setup beyond Docker.

privateGPT MCP Server

一个服务器实现，允许 MCP 客户端和 privateGPT 之间进行安全通信，使用户能够通过知识库与 privateGPT 聊天，并通过标准化的模型上下文协议管理来源、群组和用户。