发现优秀的 MCP 服务器

Semrush MCP Server

一个模型上下文协议 (MCP) 服务器实现，它提供访问 Semrush API 数据的工具。

Memory MCP Server

一个提供知识图谱管理功能的模型上下文协议服务器。

Caiyun Weather MCP Server

彩云天气 API 的模型上下文协议 (MCP) 服务器

MCP Compliance

一个 MCP 服务器，用于支持 AI 代理中的合规性操作。 (Or, a slightly more formal/technical translation:) 一个 MCP 服务器，用于支持人工智能代理中的合规性操作。

ISO 9001 MCP Server

ISO 9001 模型上下文协议服务器实现

WhatsUpDoc (downmarked)

Okay, I understand. Here's a breakdown of how I would approach translating the request "Scrape any developer documentation and save it locally as a markdown file using anthropic's MCP to standardize communication between the cli and the documentation server" into Chinese, along with explanations to ensure clarity and accuracy: **Translation:** 使用 Anthropic 的 MCP 抓取任何开发者文档，并将其保存为本地 Markdown 文件，以标准化 CLI 和文档服务器之间的通信。 **Breakdown and Explanation:** * **"Scrape any developer documentation"**: * Chinese: 抓取任何开发者文档 (zhuā qǔ rèn hé kāi fā zhě wén dàng) * *抓取 (zhuā qǔ)*: This is a common and direct translation for "scrape" in the context of web data. It implies extracting information. * *任何 (rèn hé)*: "Any" * *开发者文档 (kāi fā zhě wén dàng)*: "Developer documentation" - a standard and clear term. * **"and save it locally as a markdown file"**: * Chinese: 并将其保存为本地 Markdown 文件 (bìng jiāng qí bǎo cún wèi běn dì Markdown wén jiàn) * *并 (bìng)*: "And" (connective word) * *将其 (jiāng qí)*: "It" (referring to the scraped documentation). Using "将其" is more formal and grammatically correct in this context than just "它 (tā)". * *保存为 (bǎo cún wèi)*: "Save as" * *本地 (běn dì)*: "Locally" * *Markdown 文件 (Markdown wén jiàn)*: "Markdown file" - the English term "Markdown" is commonly used in Chinese technical contexts. "文件 (wén jiàn)" means "file". * **"using anthropic's MCP"**: * Chinese: 使用 Anthropic 的 MCP (shǐ yòng Anthropic de MCP) * *使用 (shǐ yòng)*: "Using" * *Anthropic 的 (Anthropic de)*: "Anthropic's" - possessive form. The company name is kept in English. * *MCP*: The acronym "MCP" is kept in English, assuming it's a specific product or technology name that's not commonly translated. * **"to standardize communication between the cli and the documentation server"**: * Chinese: 以标准化 CLI 和文档服务器之间的通信 (yǐ biāo zhǔn huà CLI hé wén dàng fú wù qì zhī jiān de tōng xìn) * *以 (yǐ)*: "In order to", "to" (expressing purpose) * *标准化 (biāo zhǔn huà)*: "Standardize" * *CLI*: The acronym "CLI" is kept in English, as it's a common technical term. * *和 (hé)*: "And" * *文档服务器 (wén dàng fú wù qì)*: "Documentation server" * *之间 (zhī jiān)*: "Between" * *的 (de)*: Possessive particle, linking "通信 (tōng xìn)" to the preceding phrase. * *通信 (tōng xìn)*: "Communication" **Putting it all together:** The final translated sentence is: 使用 Anthropic 的 MCP 抓取任何开发者文档，并将其保存为本地 Markdown 文件，以标准化 CLI 和文档服务器之间的通信。 **Important Considerations:** * **Context is Key:** The best translation always depends on the specific context. If "MCP" has a well-known Chinese translation within the Anthropic ecosystem, that should be used instead of keeping the acronym. * **Target Audience:** Consider the technical proficiency of the target audience. If they are very familiar with English technical terms, keeping more terms in English might be acceptable. * **Consistency:** If this is part of a larger project, ensure consistency in terminology with other translations. This detailed breakdown should provide a clear and accurate translation of the original English request. Let me know if you have any other questions or need further refinement!

mcp_repob7b7df37-94c2-48e4-8721-6cc695c23d4c

这是一个由 MCP 服务器的测试脚本为 GitHub 创建的测试仓库。

@f4ww4z/mcp-mysql-server

镜子 (jìng zi)

ThemeParks.wiki API MCP Server

主题公园维基 API MCP 服务器

MCP OpenMetadata

提供 OpenMetadata API 的 MCP 服务器

ChatSum

Okay, I can help with that. To summarize WeChat messages, I need you to provide me with the text of the messages. Please paste the WeChat conversation here, and I will do my best to provide a concise and accurate summary in Chinese. For example, you can paste something like this: **Example Input:** ``` Person A: Hey, are you free for lunch tomorrow? Person B: Yeah, I think so. Where do you want to go? Person A: How about that new Italian place downtown? Person B: Sounds good! What time? Person A: Noon? Person B: Perfect! See you then. ``` Then I will provide a summary in Chinese. **The more context you give me, the better the summary will be.** For example, if you tell me the topic of the conversation beforehand, I can focus the summary on that. Looking forward to helping you!

MCP Etherscan Server

镜子 (jìng zi)

arXiv Search MCP Server

一个 MCP 服务器，提供从 arXiv.org 搜索和获取论文的工具。

Google Search Console MCP Server

它通过官方 API 直接连接到您的 Google Search Console 帐户，让您可以直接从 AI 工具（如 Claude Desktop 或 OpenAI Agents SDK 等）访问关键数据。

PDF Reader MCP Server (@shtse8/pdf-reader-mcp)

一个使用 Node.js/TypeScript 构建的 MCP 服务器，允许 AI 代理安全地读取 PDF 文件（本地或 URL），并提取文本、元数据或页数。使用 pdf-parse 库。

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。

focus_mcp_data

DataFocus 下的智能数据查询插件支持多轮对话，提供即插即用的 ChatBI 功能。

Semantic Scholar MCP Server

镜子 (jìng zi)

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)

MCP with Gemini Tutorial

使用 Google Gemini 构建 MCP 服务器