Discover Awesome MCP Servers

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.

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.

PocketBase MCP Server

Enables MCP-compatible applications to directly interact with PocketBase databases for collection management, record operations, schema generation, and data analysis.

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.

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.

Claude Deep Think MCP Server

Provides proactive deep analytical thinking using Claude Sonnet 4.5 before writing code, helping analyze errors, requirements, and architectural decisions to produce better quality code with fewer iterations.

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, I can help you understand how to turn a Robot Framework library into an MCP (Message Center Protocol) server. It's a bit of a complex process, but here's a breakdown of the concepts and steps involved, along with considerations: **Understanding the Goal** First, let's clarify what we mean by "turning a Robot Framework library into an MCP server." Essentially, you want to expose the functionality of your Robot Framework library so that other applications (clients) can access and use it remotely via the MCP protocol. **Key Concepts** * **Robot Framework Library:** A collection of keywords (functions) that can be used in Robot Framework test cases. * **MCP (Message Center Protocol):** A communication protocol used for exchanging messages between applications. It's often used in embedded systems and other scenarios where a lightweight, reliable communication mechanism is needed. It defines how messages are formatted, sent, and received. * **Server:** A program that listens for incoming requests from clients and provides services in response. In this case, the server will receive MCP messages, interpret them as requests to execute Robot Framework library keywords, and send back the results as MCP messages. * **Client:** A program that sends requests to the server. **General Steps** Here's a high-level outline of the steps involved: 1. **Choose an MCP Implementation/Library:** You'll need a library or framework that handles the MCP protocol details (message encoding/decoding, connection management, etc.). There isn't a single, universally standard MCP library, so you'll need to find one that suits your needs and programming language. If you're using Python (which is common with Robot Framework), you might need to adapt an existing MCP implementation or create your own. 2. **Create a Server Application:** This will be the core of your MCP server. It will: * **Listen for Incoming Connections:** Set up a socket to listen for incoming TCP/IP connections from MCP clients. * **Receive MCP Messages:** Receive and decode MCP messages from clients. * **Parse MCP Messages:** Determine which Robot Framework keyword the client is requesting to execute and extract any arguments. * **Execute Robot Framework Keywords:** Call the appropriate keyword from your Robot Framework library with the provided arguments. * **Format Results as MCP Messages:** Take the results returned by the Robot Framework keyword (success/failure, return values) and encode them into MCP messages. * **Send MCP Response:** Send the MCP response message back to the client. * **Handle Errors:** Gracefully handle errors that occur during message processing or keyword execution. 3. **Map MCP Messages to Robot Framework Keywords:** You'll need a mechanism to map incoming MCP messages to specific keywords in your Robot Framework library. This could be a simple lookup table or a more sophisticated routing system. 4. **Implement Data Serialization/Deserialization:** MCP messages are typically byte streams. You'll need to serialize data (arguments to keywords, return values) into a format suitable for transmission over MCP and deserialize it on the receiving end. Common serialization formats include: * **JSON:** Human-readable and widely supported. * **Protocol Buffers (protobuf):** Efficient and language-neutral. * **MessagePack:** Another efficient binary serialization format. * **Custom Binary Format:** If you need maximum performance or have very specific requirements, you could define your own binary format. 5. **Error Handling:** Implement robust error handling to catch exceptions during keyword execution, message parsing, or network communication. Send appropriate error messages back to the client via MCP. **Example (Conceptual - Python)** ```python # This is a simplified example and requires a real MCP library # and proper error handling. import socket import json from robot.api import logger # For Robot Framework logging # Assume you have a Robot Framework library called 'MyLibrary' from MyLibrary import MyLibrary # MCP Configuration HOST = '127.0.0.1' # Standard loopback interface address (localhost) PORT = 65432 # Port to listen on (non-privileged ports are > 1023) # Initialize Robot Framework library my_library = MyLibrary() def handle_client(conn, addr): print(f"Connected by {addr}") while True: data = conn.recv(1024) # Receive data from the client if not data: break try: # Decode MCP message (assuming JSON for simplicity) message = json.loads(data.decode('utf-8')) keyword = message['keyword'] args = message.get('args', []) # Arguments are optional # Execute Robot Framework keyword try: result = getattr(my_library, keyword)(*args) # Call the keyword response = {'status': 'success', 'result': result} except Exception as e: logger.error(f"Error executing keyword: {e}") response = {'status': 'error', 'message': str(e)} # Encode response as MCP message (JSON) response_data = json.dumps(response).encode('utf-8') conn.sendall(response_data) except json.JSONDecodeError: error_message = {'status': 'error', 'message': 'Invalid JSON'} conn.sendall(json.dumps(error_message).encode('utf-8')) except AttributeError: error_message = {'status': 'error', 'message': f'Keyword "{keyword}" not found'} conn.sendall(json.dumps(error_message).encode('utf-8')) except Exception as e: logger.exception("Unexpected error") error_message = {'status': 'error', 'message': f'Internal server error: {e}'} conn.sendall(json.dumps(error_message).encode('utf-8')) conn.close() print(f"Connection closed with {addr}") def start_server(): with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind((HOST, PORT)) s.listen() print(f"Listening on {HOST}:{PORT}") while True: conn, addr = s.accept() handle_client(conn, addr) # Handle each client in a separate thread for concurrency (recommended) if __name__ == "__main__": start_server() ``` **Important Considerations** * **Security:** If you're exposing your Robot Framework library over a network, security is paramount. Consider: * **Authentication:** Verify the identity of clients before allowing them to execute keywords. * **Authorization:** Control which clients have access to which keywords. * **Encryption:** Encrypt the MCP messages to protect sensitive data in transit (e.g., using TLS/SSL). * **Concurrency:** If you expect multiple clients to connect to your server simultaneously, you'll need to handle concurrency properly (e.g., using threads or asynchronous programming). The example above shows a basic single-threaded server. For production, you'd want to use threads or asyncio. * **Error Handling:** Implement robust error handling to catch exceptions during keyword execution, message parsing, or network communication. Send appropriate error messages back to the client via MCP. * **Data Types:** Carefully consider how you'll handle data types when passing arguments to keywords and returning results. MCP typically deals with byte streams, so you'll need to serialize and deserialize data appropriately. * **Performance:** If performance is critical, choose efficient serialization formats and optimize your code. * **MCP Library Choice:** The choice of MCP library will significantly impact the complexity of your implementation. If a suitable library doesn't exist, you might need to implement the MCP protocol yourself, which is a non-trivial task. **In summary:** Turning a Robot Framework library into an MCP server involves creating a server application that listens for MCP messages, parses them to determine which Robot Framework keyword to execute, executes the keyword, and sends the results back to the client as MCP messages. You'll need to choose an MCP library, implement data serialization/deserialization, and handle concurrency and security. The complexity of the task depends on the specific requirements of your application and the availability of suitable MCP libraries. The example code provides a basic starting point, but it needs to be adapted and extended to meet the needs of a real-world application. Remember to prioritize security and error handling.

Fusion 360 MCP Integration

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

MCP Bridge API

MCP Bridge es un proxy ligero, rápido y agnóstico a LLM para conectarse a múltiples servidores del Protocolo de Contexto de Modelos (MCP) a través de una API REST unificada. Permite la ejecución segura de herramientas en diversos entornos como dispositivos móviles, web y periféricos. Diseñado para la flexibilidad, la escalabilidad y la fácil integración con cualquier backend de LLM.

ITSM Integration Platform

Here are a few possible translations of "MCP for ITSM tool integration," depending on the context. "MCP" is the tricky part, as it could stand for several things. I'll provide options based on the most likely interpretations: **Assuming "MCP" stands for "Managed Cloud Provider":** * **Integración de herramientas ITSM con un proveedor de nube gestionada.** (This is a general translation.) * **Integración de herramientas ITSM a través de un proveedor de nube gestionada.** (This emphasizes the "through" aspect.) **Assuming "MCP" stands for "Microsoft Certified Professional" (less likely in this context, but possible):** * **Integración de herramientas ITSM con un profesional certificado de Microsoft.** (This is a literal translation, but might not be the best if you're talking about a *solution* rather than a person.) * **Integración de herramientas ITSM con una solución basada en la certificación Microsoft.** (This is a more nuanced translation, suggesting the integration leverages Microsoft certification standards.) **Assuming "MCP" is an acronym specific to the company or project:** * **Integración de herramientas ITSM con [Nombre completo de MCP].** (The best option if you know what MCP stands for. For example: "Integración de herramientas ITSM con el Módulo de Control de Procesos.") **General Options (if you don't know what MCP stands for and need a placeholder):** * **Integración de herramientas ITSM con MCP.** (Simply leaves the acronym as is. This is fine if the audience knows what MCP means.) * **Integración de herramientas ITSM con [Sistema/Plataforma/Herramienta] MCP.** (This is a more descriptive placeholder, suggesting what MCP *might* be.) **Therefore, to give you the *best* translation, I need to know what "MCP" stands for in your context.** Please provide more information!

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.

FLUX MCP Server

Exposes Replicate's FLUX image generation models to Claude, enabling text-to-image generation, image variations, inpainting, and edge-guided creation with 6 different FLUX models.

Vercel MCP Relay Server

Enables AI assistants to interact with the Vercel REST API to manage projects, deployments, domains, environment variables, and teams through natural language commands.

Gemini RAG MCP Server

Enables creation and querying of knowledge bases using Google's Gemini API File Search feature, allowing AI applications to upload documents and retrieve information through RAG (Retrieval-Augmented Generation).

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.

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.

Overwatch2 MCP Server

An MCP server that provides Overwatch 2 hero data, allowing users to retrieve hero lists and detailed information about specific heroes.

Whoop MCP Server

Espejo de

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.

Chess MCP Server

Enables AI assistants to play, analyze, and track chess games with full rule validation and support for standard algebraic notation. It provides tools for position evaluation and game state persistence during user sessions.

rekordbox-mcp

MCP server for rekordbox DJ database access. Provides read-only querying of tracks, playlists, and DJ session history from encrypted rekordbox SQLite databases using pyrekordbox.

Advertising-Analysis

A demonstration project of an MCP server for injecting advertisements into LLM responses, showcasing the risks of advertising injection middleware.

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.

mcp-book-search

Servidor MCP para la búsqueda de libros nacionales

Python Server MCP

Un servicio de precios de criptomonedas que proporciona información de precios de criptomonedas en tiempo real a través de un marco MCP (Protocolo de Contexto de Modelo) con integración de la API de CoinMarketCap.