发现优秀的 MCP 服务器

NebulaMind

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mcp-server-macos-use

能够使用操作系统级别工具控制计算机的人工智能代理，兼容 MCP，并且可以与任何模型一起使用。

Weather MCP Server

A Model Context Protocol server that enables AI assistants to fetch current weather, forecasts, and search for locations using WeatherAPI service through stdio communication.

TypeScript MCP Server Boilerplate

A foundational project for quickly developing Model Context Protocol servers using the TypeScript SDK and Zod for schema validation. It features pre-implemented examples for tools like a calculator and greeting service, along with templates for managing dynamic resources.

Git MCP Server

Enables git repository operations through REST endpoints, providing access to repository status, diffs, and commits. Enforces security through configurable root directory allowlists for safe git operations.

Jira MCP Server

Enables AI assistants to interact with Atlassian Jira Cloud, allowing users to manage projects, issues, comments, and workflows through natural language commands.

Kali SSE MCP Command Executor

Enables secure execution of penetration testing commands on Kali Linux through Server-Sent Events with intelligent command validation, real-time monitoring, and comprehensive audit logging. Designed for authorized security research and penetration testing workflows.

mcp-otle

An MCP server that simulates the Chipotle ordering experience, allowing AI agents to browse menus, build entrees, and analyze burrito structural integrity. It provides a humorous take on 'Guac-as-a-Service' through tools for order simulation and nutritional assessments.

Custom Search MCP Server

An MCP server that enables interaction with Google Custom Search API through natural language, allowing users to perform searches programmatically via the Multi-Agent Conversation Protocol.

Daily Briefing MCP Server

Aggregates data from Google Calendar, TripIt, and Fireflies.ai to generate comprehensive daily briefings, schedule overviews, and action item lists. It enables users to manage their time by detecting meeting conflicts, identifying focus slots, and tracking upcoming travel plans.

github-server MCP Server

镜子 (jìng zi)

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.

android-emulator-mcp

An MCP server for managing local Android Virtual Devices (AVDs) and their lifecycles using Android SDK tools. It enables users to create, start, and stop emulators, while providing control over virtual sensors like the accelerometer and gyroscope.

Iceberg MCP

Apache Iceberg 的 MCP 服务器

LSP MCP Server

Provides code refactoring capabilities for TypeScript/JavaScript and Python through Language Server Protocol integration. Enables renaming symbols, extracting functions, finding references, and moving code between files via natural language commands.

TAPD Data Fetcher

A Python MCP server that retrieves requirements and bug data from TAPD platform to provide AI clients with project management information.

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。

Bloomberg MCP

一个提供来自彭博 (Bloomberg) blpapi 的金融数据的 MCP 服务器。 (Or, more formally:) 一个提供来自彭博 (Bloomberg) blpapi 的金融数据的 MCP 服务器。

Gmail MCP

Enables comprehensive Gmail management through the Gmail API, including sending/receiving emails, organizing labels and threads, managing drafts, and configuring account settings with secure OAuth2 authentication.

Filesystem MCP Server SSE

MCP 服务的 SSE 版本是从文件系统 MCP 服务器修改而来的。

Canvelete MCP Server

Enables programmatic design creation and manipulation on the Canvelete platform through AI assistants. Supports design management, canvas element manipulation, template application, asset management, and real-time synchronization with the design editor.

Jokes MCP Server

Enables fetching jokes from multiple APIs including Chuck Norris jokes, Dad jokes, and Yo Mama jokes. Provides a comprehensive joke-telling experience through Microsoft Copilot Studio integration.

Geth MCP Proxy

Bridges Ethereum JSON-RPC queries from Geth nodes to the Model Context Protocol ecosystem, exposing blockchain operations as MCP tools. Enables AI models and applications to securely interact with Ethereum data including blocks, transactions, balances, and advanced debug functions through schema-validated access.

Hevy MCP Server

Enables interaction with the Hevy fitness tracking platform through their API. Supports managing workouts, routines, exercise templates, and webhook subscriptions for comprehensive fitness data management.

mcp-dbutils

DButils 是一项一体化的 MCP 服务，它使您的 AI 能够通过统一的连接配置，以安全的方式访问各种类型的数据库（sqlite、mysql、postgres 等）来进行数据分析。

MCP Web Research Agent

Enables automated web research and intelligence gathering through recursive web crawling, multi-engine search integration, and persistent SQLite storage with support for keyword filtering and multiple export formats.

DuckDuckGo MCP Server

一个通过模型上下文协议提供 DuckDuckGo 搜索功能（文本、图片、新闻、视频搜索和 AI 聊天）的服务器。

Higress OPS MCP Server

一个模型上下文协议服务器，它通过精心设计的代理流程架构，实现对 Higress 的全面配置和管理。

GitHub Team Management MCP Server

Enables comprehensive GitHub project management automation including creating/managing issues, updating project boards, assigning team members, and analyzing repository activity across organizations through OAuth-authenticated AI assistants.