发现优秀的 MCP 服务器

TShark2MCP

An MCP server that enables AI-assisted network packet analysis using Wireshark's TShark tool. It provides tools for pcap file overview, session extraction, protocol filtering, and statistical analysis through a standardized interface.

Cloudflare Remote MCP Server (Authless)

A deployable Model Context Protocol server for Cloudflare Workers that enables AI models to use custom tools without requiring authentication.

Google Chat MCP Server

Enables posting text messages to Google Chat spaces through webhook-based integration, providing simple and secure message delivery without OAuth setup requirements.

RateSpot MCP Server

Provides access to RateSpot.io mortgage rate APIs, enabling AI assistants to fetch real-time mortgage rates, compare loan products, calculate payments, and access comprehensive lending information.

TaskMateAI

一个由人工智能驱动的任务管理应用程序，通过MCP运行，能够自主创建、组织和执行任务，并支持子任务、优先级和进度跟踪。

Weather MCP Server

Provides real-time weather forecasts and alerts by fetching data from the National Weather Service API, allowing Claude to answer weather-related questions with up-to-date information.

Mathematica MCP Server

Allows LLMs to execute Wolfram Language code in a secure, session-based environment by providing an interface to interact with a Wolfram Mathematica kernel.

Pilldoc User MCP

Provides access to pharmaceutical management system APIs including user authentication, pharmacy account management, and advertising campaign controls. Enables querying pharmacy information, updating account details, and managing ad blocking through natural language interactions.

Cinema4D MCP Server

将 Cinema 4D 连接到 Claude，通过自然语言命令实现 AI 辅助的 3D 建模和场景操作。

MCP Server Boilerplate

A starter template for building custom MCP servers that can integrate with Claude, Cursor, or other MCP-compatible AI assistants. Provides a clean foundation with TypeScript support, example implementations, and easy installation scripts for quickly creating tools, resources, and prompt templates.

Meta MCP Server

镜子 (jìng zi)

ICON MCP v103

Provides AI agents and LLMs with secure access to the ICON MCP v103 API via Bearer tokens or HTTP 402 payment protocols. It enables standardized interaction with market data and API endpoints through the Model Context Protocol.

DaVinci Resolve MCP Server

Connects AI coding assistants (Cursor, Claude Desktop) to DaVinci Resolve, enabling control of video editing workflows through natural language commands for project management, timeline operations, and media pool tasks.

Channel Talk MCP Server

An unofficial MCP server that integrates with the Channel Talk Open API to allow AI assistants to access and manage chat information. It enables users to retrieve chat lists and message histories filtered by status, tags, and customer details.

EntityIdentification

A MCP server that helps determine if two sets of data belong to the same entity by comparing both exact and semantic equality through text normalization and language model integration.

GitHub Configuration

TickTick 任务管理应用的 Model Context Protocol (MCP) 服务器

makefilemcpserver

An MCP server that exposes Makefile targets as callable tools for AI assistants, allowing Claude and similar models to execute Make commands with provided arguments.

Diagrams MCP Server

Enables creation of infrastructure and architecture diagrams as code using the Python diagrams library, supporting 15+ cloud providers, 500+ node types, custom icons, and flowcharts with multiple output formats.

Linkup MCP Server

Provides real-time web search and webpage content fetching capabilities through Linkup's API, enabling AI assistants to access current information, news, and data from trusted sources across the web.

MCP Sheet Parser

A Model Context Protocol server designed for AI assistants to directly process spreadsheet files, enabling them to read, display, modify, and save various table formats like CSV and Excel.

Whoop MCP Server

镜子 (jìng zi)

MasterGo Magic MCP

Connects AI models to MasterGo design tools, enabling retrieval of DSL data, component documentation, and metadata from MasterGo design files for structured component development workflows.

RobotFrameworkLibrary-to-MCP

Okay, here's a breakdown of how to turn a Robot Framework library into an MCP (Message Center Protocol) server, along with explanations and considerations: **Understanding the Goal** The core idea is to expose the functionality of your Robot Framework library as a service that can be accessed remotely via MCP. This allows other systems (potentially written in different languages or running on different machines) to trigger actions within your Robot Framework library. **Key Concepts** * **Robot Framework Library:** A collection of keywords (functions) that can be used in Robot Framework test cases. * **MCP (Message Center Protocol):** A lightweight protocol for inter-process communication. It's often used for sending commands and receiving responses between different applications or services. * **MCP Server:** A process that listens for MCP requests, processes them, and sends back responses. * **MCP Client:** A process that sends MCP requests to an MCP server. * **Serialization/Deserialization:** Converting data structures (like Python objects) into a format suitable for transmission over a network (e.g., JSON) and then converting them back on the receiving end. **General Approach** 1. **Choose an MCP Library/Framework:** You'll need a Python library that handles the MCP protocol. Some options include: * **`mcp` (Python Package):** A dedicated MCP library for Python. This is likely the most direct and appropriate choice. You can install it with `pip install mcp`. * **ZeroMQ (with MCP Implementation):** ZeroMQ is a powerful messaging library that can be used to implement MCP. This is a more general-purpose solution, but it might be overkill if you only need MCP. * **Other Messaging Libraries:** You *could* potentially use other messaging libraries (like RabbitMQ or Redis Pub/Sub), but you'd need to implement the MCP protocol on top of them, which is more complex. 2. **Create an MCP Server:** Write a Python script that: * Imports your Robot Framework library. * Uses the chosen MCP library to create a server that listens on a specific port. * Registers handlers for different MCP commands. Each handler will correspond to a keyword in your Robot Framework library. * When a command is received, the handler will: * Extract the arguments from the MCP message. * Call the corresponding Robot Framework keyword with those arguments. * Capture the return value (if any) from the keyword. * Serialize the return value (e.g., to JSON). * Send the serialized result back to the MCP client as an MCP response. 3. **Create an MCP Client (if needed for testing):** Write a Python script (or use a tool like `netcat`) that: * Uses the chosen MCP library to connect to the MCP server. * Sends MCP requests with the appropriate command name and arguments. * Receives and deserializes the MCP response. * Prints or processes the result. **Example using the `mcp` Python Package** ```python # server.py (MCP Server) import mcp import json from robot.libraries.BuiltIn import BuiltIn # Or import your custom library # Instantiate your Robot Framework library (or use BuiltIn for demonstration) # my_library = MyRobotLibrary() builtin = BuiltIn() def execute_keyword(keyword_name, *args): """Executes a Robot Framework keyword and returns the result.""" try: result = builtin.run_keyword(keyword_name, *args) return result except Exception as e: return {"error": str(e)} # Handle errors gracefully class MyMCPHandler(mcp.Handler): def handle_message(self, message): """Handles incoming MCP messages.""" try: command = message.command arguments = message.arguments if command == "log_message": # Example: Expose the 'Log' keyword result = execute_keyword("Log", arguments.get("message", "")) # Pass arguments as needed return mcp.Response(result=result) elif command == "get_variable_value": #Example: Expose the 'Get Variable Value' keyword variable_name = arguments.get("name") default_value = arguments.get("default", None) result = execute_keyword("Get Variable Value", variable_name, default_value) return mcp.Response(result=result) else: return mcp.Response(error="Unknown command: {}".format(command)) except Exception as e: return mcp.Response(error=str(e)) if __name__ == "__main__": server = mcp.Server(handler=MyMCPHandler()) server.start() # Defaults to port 7000 print("MCP server started on port 7000...") try: server.join() # Keep the server running except KeyboardInterrupt: print("Shutting down server...") server.stop() ``` ```python # client.py (MCP Client - for testing) import mcp import json def send_mcp_request(command, arguments): """Sends an MCP request and returns the response.""" try: client = mcp.Client() response = client.send_message(mcp.Message(command=command, arguments=arguments)) client.close() return response.result, response.error except Exception as e: return None, str(e) if __name__ == "__main__": # Example 1: Call the 'Log' keyword result, error = send_mcp_request("log_message", {"message": "Hello from MCP!"}) if error: print("Error:", error) else: print("Log Result:", result) # Example 2: Call the 'Get Variable Value' keyword result, error = send_mcp_request("get_variable_value", {"name": "${TEMPDIR}"}) if error: print("Error:", error) else: print("Variable Value:", result) ``` **Explanation of the Example** * **`server.py`:** * Imports the `mcp` library and `robot.libraries.BuiltIn`. Replace `robot.libraries.BuiltIn` with your actual Robot Framework library. * `execute_keyword` function: This is the crucial part. It takes a keyword name and arguments, and then uses `BuiltIn().run_keyword()` (or the equivalent for your library) to execute the keyword. Error handling is included. * `MyMCPHandler`: This class inherits from `mcp.Handler` and overrides the `handle_message` method. This method is called whenever the server receives an MCP message. * It extracts the command name and arguments from the message. * It uses a series of `if/elif/else` statements to determine which Robot Framework keyword to call based on the command name. * It calls `execute_keyword` to execute the keyword. * It creates an `mcp.Response` object with the result (or an error message) and returns it. * The `if __name__ == "__main__":` block creates an `mcp.Server` instance, starts it, and keeps it running until a `KeyboardInterrupt` (Ctrl+C) is received. * **`client.py`:** * Imports the `mcp` library. * `send_mcp_request` function: This function takes a command name and arguments, creates an `mcp.Message` object, sends it to the server, and returns the response. * The `if __name__ == "__main__":` block shows how to use the `send_mcp_request` function to call the `log_message` and `get_variable_value` commands. **How to Run the Example** 1. **Install `mcp`:** `pip install mcp` 2. **Save the code:** Save the server code as `server.py` and the client code as `client.py`. 3. **Run the server:** `python server.py` 4. **Run the client:** `python client.py` (in a separate terminal) You should see the "Hello from MCP!" message logged by the Robot Framework `Log` keyword, and the value of the `${TEMPDIR}` variable printed by the client. **Important Considerations and Enhancements** * **Error Handling:** The example includes basic error handling, but you should add more robust error handling to catch exceptions and return meaningful error messages to the client. * **Security:** MCP itself doesn't provide any security features. If you need to secure your MCP server, you'll need to implement your own security mechanisms (e.g., authentication, encryption). Consider using TLS/SSL for encryption. * **Argument Handling:** The example assumes that the arguments are passed as a dictionary. You might need to adjust the argument handling to match the specific requirements of your Robot Framework keywords. Consider using a more structured data format like JSON Schema to define the expected arguments for each command. * **Data Serialization:** The example uses JSON for serialization. You can use other serialization formats (e.g., Pickle, MessagePack) if needed. JSON is generally a good choice for interoperability. * **Asynchronous Operations:** If your Robot Framework keywords perform long-running operations, consider using asynchronous programming (e.g., `asyncio`) to prevent the MCP server from blocking. * **Configuration:** Use a configuration file (e.g., YAML, JSON) to store the server's port number, logging settings, and other configuration parameters. * **Logging:** Add logging to the server to track requests, responses, and errors. Use a logging library like `logging`. * **Command Discovery:** Implement a mechanism for the client to discover the available commands and their arguments. This could be done by adding a special "describe" command to the server. * **Robot Framework Listener:** You could potentially use a Robot Framework listener to automatically register keywords as MCP commands. This would reduce the amount of manual configuration required. * **Testing:** Write unit tests and integration tests to ensure that your MCP server is working correctly. **In summary, turning a Robot Framework library into an MCP server involves creating a Python script that listens for MCP requests, calls the appropriate Robot Framework keywords, and sends back the results as MCP responses. The `mcp` Python package provides a convenient way to implement the MCP protocol.** Remember to consider error handling, security, argument handling, and other important factors to create a robust and reliable service.

Naver Search MCP Server

Provides access to Naver Search APIs, allowing AI agents to search across multiple categories (blogs, news, books, images, shopping items, etc.) with structured responses optimized for LLM consumption.

Terraform MCP Server

Enables management of Terraform infrastructure as code through 25 comprehensive tools for operations including plan/apply/destroy, state management, workspace management, and configuration file editing.

AutoCAD MCP Server

Enables AI agents to interact with AutoCAD through Python automation to draw geometric shapes like lines, circles, and polylines in real-time. It facilitates direct control of a running AutoCAD instance on Windows for basic geometric element creation.

Library Docs MCP Server

Searches and fetches real-time documentation for libraries like Langchain, OpenAI, and Llama-Index using the Serper API. It allows LLMs to access up-to-date technical information and bypass knowledge cut-off limitations.

Roblox MCP Unified Server

Comprehensive Roblox development server with 8 tools for managing Lua/Luau scripts including creation, validation, backup/restore, and project management with SQLite storage and HMAC authentication.

MusicGPT MCP Server

Provides AI-powered audio generation and processing through the MusicGPT API, enabling music creation, voice conversion, audio manipulation, stem extraction, and audio analysis capabilities.