Discover Awesome MCP Servers

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.

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.

Ping MCP Server

An MCP server that allows users to ping other hosts to check network connectivity and diagnostic information. It enables LLMs to perform network latency tests and host availability checks through a standardized interface.

MCP Webhook Server

An MCP server that enables sending data to webhooks via HTTP POST for both local and remote team environments. It provides a tool for relaying task descriptions, custom metadata, and automated notifications to external services.

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.

Fathom MCP Server

Enables AI agents to access Fathom AI meeting data including transcripts, summaries, teams, and team members. Provides tools to list meetings with filters, retrieve detailed transcripts and summaries, and manage team information.

MCP Spec Generator

Generates project specifications and file structures using Claude AI through MCP integration. Enables users to describe project ideas and automatically create corresponding documentation and project files.

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.

MCP Plus

A lightweight MCP server that enhances AI agents with tools for codebase analysis, task delegation to sub-agents, multi-agent coordination through chatrooms, and project todo management.

Cursor n8n Builder

Enables AI assistants in Cursor IDE to manage n8n workflows through the n8n REST API, including creating, updating, activating workflows, viewing execution history, and triggering webhooks.

Loop MCP Server

Enables LLMs to process arrays item-by-item or in batches with a specific task, storing and retrieving results with optional summarization after completion.

Cloudflare Remote MCP Server

A template for deploying remote Model Context Protocol servers on Cloudflare Workers using Server-Sent Events (SSE). It enables the creation and hosting of custom AI tools that can be accessed via the Cloudflare AI Playground or local clients like Claude Desktop.

MCP Server TypeScript Starter

A Model Context Protocol (MCP) server that provides location services

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.

Google Docs MCP Server

Enables Claude to read, create, and update Google Docs through OAuth authentication. Deployed as a remote Next.js server on Vercel with support for multiple users and secure API key management.

MCP Agents Server

Facilitates interaction and context sharing between AI models using the standardized Model Context Protocol (MCP) with features like interoperability, scalability, security, and flexibility across diverse AI systems.

Where's Waldo Rick

An MCP server that provides agentic vision capabilities for visual regression testing by capturing and comparing screenshots using Gemini Flash. It enables users to detect UI changes and conduct conversational investigations to distinguish between intended and unintended visual modifications.

Financial Data MCP Server

A Model Context Protocol server that provides financial tools for retrieving real-time stock data, analyst recommendations, financial statements, and web search capabilities for a LangGraph-powered ReAct agent.

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.

GMS2 MCP Server

An MCP server that parses GameMaker Studio 2 projects, providing developers and AI agents with quick access to project structure, GML code, and asset metadata.

Macroforge MCP Server

Enables AI assistants to access Macroforge documentation, validate TypeScript code with @derive decorators, expand macros, and retrieve macro documentation.

OpenFeature MCP Server

Provides OpenFeature SDK installation guidance through MCP tool calls. Enables AI clients to fetch installation prompts and setup instructions for various OpenFeature SDKs across different programming languages and frameworks.

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.

REMnux Documentation MCP Server

Enables AI assistants to search and retrieve information from the REMnux malware analysis toolkit documentation, allowing users to ask questions about reverse-engineering tools and get accurate, up-to-date answers from the live documentation.

Cross-System Agent Communication MCP Server

镜子 (jìng zi)

ThinkDrop Vision Service

Provides screen capture, OCR text extraction, and visual language model scene understanding capabilities with continuous monitoring and automatic memory storage integration.

Local MCP-Server-with-HTTPS-and-GitHub-OAuth

这个项目是一个安全的 MCP 服务器，使用 Node.js 和 Express 构建。它具有以下特性：使用自签名证书的 HTTPS 加密、GitHub OAuth 身份验证，以及额外的安全措施，例如速率限制和 HTTP 头部保护。