Discover Awesome MCP Servers

scraps-kitchen-mcp

Household-aware kitchen brain for AI agents: manage pantry inventory with freshness tracking, shopping lists, recipe collections with cook notes and per-diner ratings, dietary profiles with allergen safety, and kitchen equipment — all through 27 tools with OAuth 2.1 authentication. Includes a free tool for ingredient-based recipe generation without an account (accounts are free!).

LinkedIn MCP Server

A comprehensive Model Context Protocol server that enables AI assistants to interact with LinkedIn APIs for profile management, content creation, networking, messaging, and analytics.

MCP Spotify Server

Enables interaction with Spotify through LLMs using OAuth2 authentication. Supports music search, playback control, playlist management, and device management through natural language commands.

DX MCP Server

Enables AI applications to write and execute natural language queries on organizational data stored in DX Data Cloud's Postgres database. Runs locally and provides a query tool for AI assistants to formulate and execute database queries with user approval.

WatchBase MCP Server

一个 MCP 服务器，提供对 WatchBase 手表数据库的结构化访问，使应用程序能够查询全面的手表元数据，包括品牌、系列、型号、参考编号和技术细节。

agrobr-mcp

An MCP server that provides real-time access to Brazilian agricultural data, including commodity prices, crop estimates, climate information, and deforestation rates. It integrates data from 19 public sources like CEPEA, CONAB, and IBGE to enable LLMs to analyze the Brazilian agribusiness sector.

Link MCP

Enables intelligent web document scraping and conversation memory management for Cursor IDE. Supports multi-level link crawling, automatic content extraction, and organized storage of conversations and technical documentation.

Polarsteps MCP Server

A server that enables AI assistants to access Polarsteps travel data, allowing queries about user profiles, trip details, travel statistics, and natural language search through travel histories.

Java Class Analyzer MCP Server

Enables AI tools to analyze Java dependencies by scanning Maven projects, decompiling JAR files, and extracting detailed class information including methods, fields, and inheritance relationships. Solves the problem of AI hallucinations when generating code that calls external dependencies by providing accurate class structures through decompilation.

VSC-MCP

这个项目提供的工具可以将语言服务器协议（LSP）的功能作为 MCP（模型上下文协议）工具公开。

Snowflake MCP Server

Enables Claude to interact with Snowflake data warehouses through natural language for executing SQL queries, exploring schemas, and monitoring data freshness. It streamlines data analysis workflows by bringing Snowflake capabilities directly into the AI conversation.

DataWorks MCP Server

镜子 (jìng zi)

GitLab MCP Server

Enables AI assistants to interact with GitLab projects, allowing users to view and manage merge requests, review discussions, check CI/CD pipelines and test results, and respond to comments through natural language.

VerdictSwarm MCP Server

Enables AI agents to scan crypto tokens for rug pulls, scams, and risk using a six-agent consensus system. It provides real-time security audits and risk scoring for tokens on Solana, Ethereum, Base, and BSC.

Ultimate-MCP-Server

Provides a suite of tools like agent orchestration and token optimization for Claude

vmware-nsx

AI-powered VMware NSX networking management. Configure segments, gateways, NAT, routing, and IPAM via natural language with 31 MCP tools.

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.

Formula One MCP Server

A Model Context Protocol server that provides comprehensive Formula One racing data, enabling access to event schedules, driver information, telemetry data, race results, and performance analytics through natural language queries.

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.

Gold Standard Apology MCP

Provides guidelines for writing proper apologies based on the situation, relationship, and severity. Returns structured prompts that help LLMs generate sincere and appropriate apology letters in Korean.

Fusion 360 MCP Integration

Enables AI assistants to interact programmatically with Autodesk Fusion 360 for creating parametric 3D models through simple API calls.

MCSManager MCP Server

Enables management of Minecraft servers through the MCSManager API. Supports executing server commands, checking player status, retrieving server information, and controlling game settings like weather.

WeatherAPI MCP Server

使用 WeatherAPI 提供任何城市的当前天气和空气质量数据，可轻松与 n8n 和 Claude Desktop App 等 MCP 客户端集成。

Web-QA

An AI-powered MCP server that automates web testing workflows by enabling recording, execution, and discovery of tests through natural language prompts.

CodeMerge

A Model Context Protocol server that uses Osmosis-Apply-1.7B to intelligently apply code edits while preserving the structure of the original code.

MCP Sample Chat

A local LLM chat application implementing the Model Control Protocol (MCP) architecture with Ollama, FastAPI, and Gradio that demonstrates clear separation of model, control, and presentation layers.

NoctisAI

Enables advanced malware development, threat intelligence analysis, and offensive security operations through specialized tools for multi-language payload generation, obfuscation, OSINT reconnaissance, and forensic analysis. Designed for authorized penetration testing, red team exercises, and cybersecurity research with comprehensive educational capabilities.

Gemini 2.5 Flash Image MCP

Enables conversational image generation and editing with Google's Gemini 2.5 Flash Image Preview. Supports text-to-image generation, natural language image editing, multi-image composition, and style transfer with optional file saving.

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.