Discover Awesome MCP Servers

GitHub Configuration

Un servidor de Protocolo de Contexto de Modelo (MCP) para la aplicación de gestión de tareas TickTick.

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.

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.

HubAPI Auth MCP Server

An MCP server for interacting with HubSpot's authentication API, enabling secure authentication and authorization operations through natural language.

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

Proporciona datos actuales del clima y la calidad del aire para cualquier ciudad utilizando WeatherAPI, integrándose fácilmente con clientes MCP como n8n y la aplicación de escritorio Claude.

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.

Gmail MCP

Enables AI assistants to interact with Gmail accounts via IMAP, allowing them to list, search, read, and send emails, manage labels and folders, and access attachments through the Model Context Protocol.

Claimify

Extracts verifiable, decontextualized factual claims from text using a research-based four-stage pipeline (sentence splitting, selection, disambiguation, and decomposition). Integrates with MCP clients to enable claim extraction and verification workflows.

Selenium MCP Server

Enables AI assistants to automate web browser interactions through Selenium WebDriver. Supports multi-browser automation, element interaction, navigation, and web testing capabilities.

Memos MCP Server

Un servidor de Protocolo de Contexto de Modelo (MCP) para la API de Memos con capacidades de búsqueda, creación, recuperación y listado de etiquetas.

Ultimate-MCP-Server

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

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.

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.

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!

ThinkDrop Vision Service

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

MCP Spark Documentation Server

Provides full-text search and retrieval tools for Apache Spark documentation using SQLite FTS5 with BM25 ranking. It enables AI assistants to efficiently search, filter by section, and read specific Spark documentation pages.

GitHub MCP Server

Here are a few ways to translate "Github MCP Server to integrate with CI flows" into Spanish, with slightly different nuances: * **Opción 1 (Más directa):** Servidor MCP de Github para integrarse con flujos de CI. * **Opción 2 (Un poco más explicativa):** Servidor MCP de Github para la integración con flujos de trabajo de CI. * **Opción 3 (Enfatizando el uso):** Servidor MCP de Github para usar con flujos de CI. * **Opción 4 (Más formal):** Servidor MCP de Github para su integración en flujos de CI. **Explanation of Choices:** * **MCP:** It's likely best to leave "MCP" as is, assuming it's an acronym or specific term. If you know what it stands for, you *could* translate that, but without knowing the context, it's safer to keep it as "MCP." * **CI Flows:** "CI flows" is commonly understood in the tech world, so "flujos de CI" is a good translation. "Flujos de trabajo de CI" is a bit more explicit ("workflow" is added). * **Integrate:** "Integrar" is the direct translation of "integrate." "Integrarse" is the reflexive form, meaning "to integrate oneself" or "to be integrated." Both are valid, but "integrarse" might be slightly more common in this context. * **Para:** This is the most common translation of "to" in this context. Therefore, I would recommend **Opción 2: Servidor MCP de Github para la integración con flujos de trabajo de CI.** It's clear and accurate.

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.

ContextKeep

Provides infinite long-term memory for AI agents with persistent, searchable storage of project details, preferences, and snippets. Reduces token costs by retrieving only relevant memories while keeping all data stored locally.