发现优秀的 MCP 服务器

Mcp Mindmesh

具有场相干性的 Claude 3.7 Swarm：一个模型上下文协议 (MCP) 服务器，它协调多个专门的 Claude 3.7 Sonnet 实例，形成一个受量子启发的集群。它在模式识别、信息论和推理专家之间创建一种场相干效应，从而从集成智能中产生最佳相干响应。

Google Analytics Data API MCP Server

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🧠 Researcher MCP Server

一个用于研究和文档辅助的，使用 Perplexity AI 的模型上下文协议 (MCP) 服务器。 Or, a slightly more formal translation: 一个使用 Perplexity AI 的，用于研究和文档辅助的模型上下文协议 (MCP) 服务器。

Remote MCP Server for Analyzing Tokens using CoinGecko Market Data

使用 CoinGecko API 分析代币的 MCP 服务器。包含 RSI、EMA、MACD、布林带、黄金交叉和死亡交叉等指标。

Making MCPs Accessible for Everyone 🚀

Klavis AI (YC X25): 面向所有人的开源 MCP 基础设施

📚 MCP Docs Search Server

这是一个轻量级的、即插即用的 MCP 服务器，它使像 Claude 或 GPT 这样的大型语言模型能够动态地搜索和检索来自流行的 AI 库（如 LangChain、LlamaIndex 和 OpenAI）的最新文档。

Hana Compass API

用于所有 EHR 集成的 API 和 MCP 服务器，通过一个统一的 API 进行医学研究。

MalwareBazaar_MCP

一个由人工智能驱动的 MCP 服务器，可自主与 MalwareBazaar 对接，为授权的网络安全研究工作流程提供实时的威胁情报和样本元数据。

YouTube Music MCP Server

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testmcpgithubdemobypurna

从 MCP 服务器演示创建。

OpenAlex MCP Server

用于 AI 代理的、以结构化方式访问世界学术文献的 OpenAlex MCP 服务器的开源实现。

Mesh Agent MCP Server

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Think Tool MCP Server

一个实现了 Anthropic 的 think 工具的 MCP 服务器

UE5-MCP (Model Control Protocol)

虚幻引擎 5 的 MCP

Strategic Co-Developer MCP Server

用于人工智能记忆和认知系统的战略性协同开发者 MCP 服务器

mcp_zhitou_server

MCP 指头服务器 (MCP zhǐtou fúwùqì)

Lunchmoney MCP Server

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mcp-server-Bloom

一个集成了网络爬取功能的模型上下文协议（MCP）服务器实现。

VNStock MCP Server

Okay, here's a breakdown of how you could create an MCP (presumably meaning a Minimal, Complete, and Verifiable example) server in Python to fetch historical stock prices using the `vnstock` library, along with explanations and considerations: ```python # server.py (or whatever you want to name your server file) from flask import Flask, request, jsonify import vnstock import datetime app = Flask(__name__) @app.route('/historical_stock_data', methods=['GET']) def get_historical_data(): """ Fetches historical stock data for a given ticker symbol and date range using the vnstock library. Query Parameters: ticker (str): The stock ticker symbol (e.g., 'VIC'). Required. start_date (str): The start date in 'YYYY-MM-DD' format. Required. end_date (str): The end date in 'YYYY-MM-DD' format. Defaults to today if not provided. Returns: JSON: A JSON response containing the historical stock data as a list of dictionaries, or an error message if there's an issue. """ ticker = request.args.get('ticker') start_date = request.args.get('start_date') end_date = request.args.get('end_date') if not ticker: return jsonify({'error': 'Missing ticker parameter'}), 400 # Bad Request if not start_date: return jsonify({'error': 'Missing start_date parameter'}), 400 try: datetime.datetime.strptime(start_date, '%Y-%m-%d') # Validate start_date format if end_date: datetime.datetime.strptime(end_date, '%Y-%m-%d') # Validate end_date format else: end_date = datetime.date.today().strftime('%Y-%m-%d') # Default to today except ValueError: return jsonify({'error': 'Invalid date format. Use YYYY-MM-DD.'}), 400 try: data = vnstock.stock_historical_data( symbol=ticker, start_date=start_date, end_date=end_date, period='1D' # Daily data ) # Convert DataFrame to list of dictionaries for JSON serialization data_list = data.to_dict(orient='records') return jsonify(data_list), 200 # OK except Exception as e: print(f"Error fetching data: {e}") # Log the error for debugging return jsonify({'error': f'Error fetching data: {str(e)}'}), 500 # Internal Server Error if __name__ == '__main__': app.run(debug=True, host='0.0.0.0', port=5000) # Important for network access ``` **Explanation and Key Improvements:** 1. **Dependencies:** - Make sure you have the necessary libraries installed: ```bash pip install flask vnstock ``` 2. **Flask Setup:** - `Flask` is a lightweight Python web framework. We create a Flask app instance. 3. **Route Definition (`/historical_stock_data`):** - `@app.route('/historical_stock_data', methods=['GET'])`: This defines a route that listens for GET requests at the `/historical_stock_data` endpoint. GET is appropriate for fetching data. 4. **Query Parameters:** - `request.args.get('ticker')`: This retrieves the `ticker`, `start_date`, and `end_date` from the URL's query parameters. For example: - `http://localhost:5000/historical_stock_data?ticker=VIC&start_date=2023-01-01&end_date=2023-01-10` 5. **Input Validation:** - **Required Parameters:** Checks if `ticker` and `start_date` are provided. Returns a 400 error (Bad Request) if they are missing. - **Date Format Validation:** Uses `datetime.datetime.strptime` to validate that `start_date` and `end_date` are in the correct `YYYY-MM-DD` format. Returns a 400 error if the format is invalid. - **Default `end_date`:** If `end_date` is not provided, it defaults to the current date. 6. **`vnstock.stock_historical_data()` Call:** - `vnstock.stock_historical_data(...)`: This is where the `vnstock` library is used to fetch the historical data. The `symbol`, `start_date`, and `end_date` are passed as arguments. `period='1D'` specifies daily data. 7. **Error Handling:** - `try...except`: A `try...except` block is used to catch potential errors during the `vnstock` data fetching process. This is crucial for a robust server. - **Logging:** `print(f"Error fetching data: {e}")` logs the error to the console. This is very helpful for debugging. In a production environment, you'd want to use a more sophisticated logging system. - **Error Response:** If an error occurs, a JSON response with an error message and a 500 status code (Internal Server Error) is returned. 8. **JSON Response:** - `data.to_dict(orient='records')`: Converts the Pandas DataFrame returned by `vnstock` into a list of dictionaries. This is necessary because Flask's `jsonify` function can easily serialize lists of dictionaries into JSON. - `jsonify(data_list)`: Converts the list of dictionaries into a JSON response. - `return jsonify(data_list), 200`: Returns the JSON response with a 200 status code (OK). 9. **Running the App:** - `if __name__ == '__main__':`: This ensures that the app is only run when the script is executed directly (not when it's imported as a module). - `app.run(debug=True, host='0.0.0.0', port=5000)`: - `debug=True`: Enables debug mode, which provides helpful error messages and automatic reloading when you make changes to the code. **Important:** Disable debug mode in production. - `host='0.0.0.0'`: This makes the server accessible from any IP address on your network. If you only want to access it from your local machine, use `host='127.0.0.1'`. - `port=5000`: Specifies the port number the server will listen on. **How to Run:** 1. **Save:** Save the code as `server.py` (or any name you prefer). 2. **Install:** Make sure you have Flask and vnstock installed (`pip install flask vnstock`). 3. **Run:** Open a terminal or command prompt, navigate to the directory where you saved the file, and run: `python server.py` 4. **Access:** Open a web browser or use a tool like `curl` or `Postman` to access the server. For example: ``` http://localhost:5000/historical_stock_data?ticker=VIC&start_date=2023-01-01&end_date=2023-01-10 ``` **Important Considerations:** * **Error Handling:** The error handling in this example is basic. In a production environment, you'd want to implement more robust error handling, including logging to a file, sending error notifications, and potentially retrying failed requests. * **Security:** This is a very basic example and doesn't include any security measures. If you're deploying this to a public server, you'll need to consider security aspects like authentication, authorization, and input validation to prevent malicious attacks. * **Rate Limiting:** Be mindful of the API usage limits of the `vnstock` library or the underlying data source. Implement rate limiting in your server to avoid being blocked. * **Asynchronous Operations:** For handling multiple concurrent requests efficiently, consider using asynchronous frameworks like `asyncio` and `aiohttp` instead of Flask. * **Configuration:** Use environment variables or a configuration file to store sensitive information like API keys or database credentials. * **Deployment:** Consider using a production-ready web server like Gunicorn or uWSGI to deploy your Flask application. **Chinese Translation of Key Terms:** * **Stock Price:** 股票价格 (gǔpiào jiàgé) * **Historical Data:** 历史数据 (lìshǐ shùjù) * **Ticker Symbol:** 股票代码 (gǔpiào dàimǎ) * **Start Date:** 开始日期 (kāishǐ rìqí) * **End Date:** 结束日期 (jiéshù rìqí) * **Server:** 服务器 (fúwùqì) * **API:** 应用程序接口 (yìngyòng chéngxù jiēkǒu) * **JSON:** JSON 数据格式 (JSON shùjù géshì) * **Error:** 错误 (cuòwù) * **Request:** 请求 (qǐngqiú) * **Response:** 响应 (xiǎngyìng) This comprehensive example should give you a solid foundation for building your stock price API using `vnstock` and Flask. Remember to adapt it to your specific needs and consider the important considerations mentioned above.

MikeCreighton.com Content MCP Server

一个模型上下文协议 (MCP) 服务器，它将把 Mike Creighton Consulting 网站的所有页面作为资源提供给任何 MCP 客户端。

Sunwood Ai Labs_mcp Weather Service Server

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automcp

轻松地将现有代理框架中的工具、代理和编排器转换为 MCP 服务器。