Discover Awesome MCP Servers

➡️ browser-use mcp server

一个 MCP 服务器，它使 AI 助手能够通过自然语言命令控制网络浏览器，从而允许它们通过 SSE 传输来浏览网站和提取信息。

MCP Server Gateway

MCP SSE Server 的网关演示

OneSignal MCP Server

一个模型上下文协议服务器，它封装了 OneSignal REST API，从而能够跨多个 OneSignal 应用程序管理推送通知、电子邮件、短信、用户设备和用户分群。

Dynamic Shell Server

一个模型上下文协议 (MCP) 服务器，它支持安全地执行 shell 命令，并具有动态审批系统。 该服务器允许运行任意命令，同时通过用户审批和审计日志记录来维护安全性。

quickchart-server MCP Server

一个 MCP 服务器，用于使用 QuickChart.io 生成可定制的数据可视化图表，支持多种图表类型和 Chart.js 配置。

Model Context Protocol (MCP)

模型上下文协议 (MCP) 是一种开放标准，它使开发者能够在他们的数据源和人工智能驱动的工具之间建立安全的双向连接。该架构非常简单：开发者可以通过 MCP 服务器公开他们的数据，或者构建连接到这些服务器的 AI 应用程序（MCP 客户端）。

MCP GO Tools

一个以 Go 语言为中心的模型上下文协议 (MCP) 服务器，它提供符合 Go 语言习惯的代码生成、风格指南和最佳实践。此工具帮助语言模型理解并生成遵循既定模式和规范的高质量 Go 代码。

Math-MCP

一个模型上下文协议服务器，它为大型语言模型（LLM）提供基本的数学和统计函数，使它们能够通过一个简单的API执行准确的数值计算。

Linear MCP Server

一个服务器，允许 AI 助手通过模型上下文协议 (MCP) 标准访问和检索 Linear 工单数据，目前专注于获取用户的待办工单。

Github Action Trigger Mcp

一个模型上下文协议服务器，可以与 GitHub Actions 集成，允许用户获取可用的 Actions、获取关于特定 Actions 的详细信息、触发工作流分发事件以及获取仓库发布版本。

Kafka MCP Server

通过一个标准化的接口，使人工智能模型能够发布和消费来自 Apache Kafka 主题的消息，从而轻松地将 Kafka 消息传递与 LLM 和代理应用程序集成。

Semantic Scholar MCP Server

镜子 (jìng zi)

PHP MCP Protocol Server

MCP 通用 PHP 服务器 - 将 PHP 与模型上下文协议集成

MCP Image Generation Server

一个用 Go 实现的 MCP (模型上下文协议) 服务器工具

Data.gov MCP Server

镜子 (jìng zi)

HANA Cloud MCP Server

镜子 (jìng zi)

better-auth-mcp-server MCP Server

镜子 (jìng zi)

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.

Google Home MCP Server

镜子 (jìng zi)

mcp-server-restart

镜子 (jìng zi)

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!

NN-GitHubTestRepo

从 MCP 服务器演示创建。

MCP 服务器示例

一个带有 WebUI 的 MCP 服务器快速演示 (Yī gè dài yǒu WebUI de MCP fúwùqì kuàisù yǎnshì)

MCP with Gemini Tutorial

使用 Google Gemini 构建 MCP 服务器