Discover Awesome MCP Servers

Pega DX MCP Server

Transforms complex Pega Platform interactions into intuitive, conversational experiences by exposing Pega DX APIs through the standardized Model Context Protocol, enabling AI applications to interact with Pega through natural language.

JinaAI Search

Permite una integración eficiente de búsqueda web con la API de búsqueda de Jina.ai, ofreciendo una recuperación de contenido limpia y optimizada para LLM, con soporte para varios tipos de contenido y almacenamiento en caché configurable.

MCP Client for Testing

Un cliente MCP minimalista para probar el servidor MCP.

DeepSeek Thinking with Claude 3.5 Sonnet

Facilita procesos de razonamiento en dos etapas utilizando DeepSeek para un análisis detallado y admite múltiples modelos de respuesta como Claude 3.5 Sonnet y OpenRouter, manteniendo el contexto de la conversación y mejorando las interacciones impulsadas por la IA.

Kakao Mobility & Kakao Map MCP Server

Mirror of

MCP Calculator Streamable HTTP

Provides basic arithmetic calculation tools through an HTTP-accessible MCP server. Supports mathematical operations like addition with streamable responses for integration with MCP clients.

Claude Printer MCP

Un servidor MCP sencillo para imprimir archivos desde Claude.

AI Pull Request Generator

An AI-powered FastMCP server tool that automates the process of planning tasks, generating code, and creating GitHub pull requests.

Debugg AI MCP

Debugg AI MCP

ClickFunnels MCP Framework

Un servidor de Protocolo de Contexto de Modelo que integra ClickFunnels con Claude Desktop, permitiendo a los usuarios listar y recuperar embudos y contactos de su cuenta de ClickFunnels a través del lenguaje natural.

JMeter MCP Server

Un servidor de Protocolo de Contexto de Modelo que permite a los asistentes de IA ejecutar y gestionar pruebas de rendimiento de JMeter a través de comandos en lenguaje natural.

AI Agent with MCP

Okay, here's a basic outline and code snippets to help you create your first MCP (Model Context Protocol) server in a Playground environment. Keep in mind that MCP is a relatively new and evolving protocol, so the specific libraries and implementations might change. This example focuses on a simplified, conceptual approach. **Conceptual Overview** 1. **Choose a Language/Framework:** Python is a good choice for rapid prototyping and has libraries suitable for networking and data serialization. 2. **Define Your Model:** Decide what kind of model you want to serve. For a simple example, let's imagine a model that performs basic arithmetic (addition). 3. **Implement the MCP Server:** * Listen for incoming connections. * Receive MCP requests. * Parse the requests. * Execute the model (in our case, addition). * Format the response according to MCP. * Send the response. 4. **Implement a Simple MCP Client (for testing):** * Create a client to send requests to your server. * Receive and parse the responses. **Simplified Python Example (using `socket` and basic JSON)** ```python # server.py (This would run in your Playground) import socket import json HOST = '127.0.0.1' # Standard loopback interface address (localhost) PORT = 65432 # Port to listen on (non-privileged ports are > 1023) def handle_request(data): """ Simulates a simple model that performs addition. Assumes the data is a JSON string with 'a' and 'b' keys. """ try: request = json.loads(data.decode('utf-8')) a = request.get('a') b = request.get('b') if a is None or b is None: return json.dumps({"error": "Missing 'a' or 'b' parameter"}).encode('utf-8') try: result = a + b response = {"result": result} return json.dumps(response).encode('utf-8') except TypeError: return json.dumps({"error": "Invalid 'a' or 'b' value (must be numbers)"}).encode('utf-8') except json.JSONDecodeError: return json.dumps({"error": "Invalid JSON"}).encode('utf-8') with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind((HOST, PORT)) s.listen() print(f"Server listening on {HOST}:{PORT}") conn, addr = s.accept() with conn: print(f"Connected by {addr}") while True: data = conn.recv(1024) # Receive up to 1024 bytes if not data: break # Client disconnected response = handle_request(data) conn.sendall(response) # Send the response back to the client ``` ```python # client.py (This would run in a separate Playground or terminal) import socket import json HOST = '127.0.0.1' # The server's hostname or IP address PORT = 65432 # The port used by the server with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.connect((HOST, PORT)) # Example request (MCP-like, but simplified) request_data = {"a": 5, "b": 3} request_json = json.dumps(request_data).encode('utf-8') s.sendall(request_json) data = s.recv(1024) print('Received:', repr(data.decode('utf-8'))) ``` **Explanation and Key Points:** * **`server.py`:** * Sets up a basic TCP socket server. * `handle_request()`: This is where your "model" logic goes. In this example, it's a simple addition function. It receives JSON data, parses it, performs the addition, and returns a JSON response. Error handling is included. * The server listens for connections, accepts a connection, and then enters a loop to receive data, process it, and send a response. * **`client.py`:** * Creates a TCP socket client. * Connects to the server. * Constructs a JSON request (representing an MCP-like request). * Sends the request to the server. * Receives the response and prints it. * **JSON for Serialization:** JSON is used for encoding and decoding the requests and responses. This is a common and relatively simple way to handle data serialization. * **Error Handling:** Basic error handling is included in the `handle_request` function to catch invalid JSON, missing parameters, and type errors. * **Simplified MCP:** This is *not* a full MCP implementation. It's a simplified example to illustrate the basic concepts. A real MCP implementation would involve more complex message structures, metadata, and potentially other protocols for data transfer. * **Playground Considerations:** Make sure your Playground environment allows network connections. Some online Playgrounds might have restrictions. If you have issues, try running the server and client on your local machine. * **Running the Code:** 1. Run `server.py` in one Playground or terminal window. 2. Run `client.py` in another Playground or terminal window. 3. The client should connect to the server, send the request, and print the response. **To make this more like a real MCP server, you would need to:** * **Define a formal MCP message structure:** MCP has specific requirements for the format of requests and responses, including metadata and data encoding. You'd need to adhere to those specifications. * **Implement a more sophisticated model:** Replace the simple addition with a more complex machine learning model. You might use libraries like TensorFlow, PyTorch, or scikit-learn. * **Handle different data types:** MCP needs to support various data types (images, text, etc.). You'd need to implement appropriate serialization and deserialization methods. * **Add authentication and authorization:** Secure your server to prevent unauthorized access. * **Consider performance:** Optimize your code for speed and efficiency, especially if you're serving a high volume of requests. **Important Considerations for Playgrounds:** * **Network Access:** Many online Playgrounds have limited or no network access. If you can't get the server and client to connect, it's likely a network restriction. Try running the code locally on your machine. * **Dependencies:** Make sure your Playground environment has the necessary libraries installed (e.g., `json`). If not, you might need to install them using `pip` or a similar package manager. * **File System Access:** Some Playgrounds might restrict file system access. If you need to load model files, you might need to find alternative ways to store and access them (e.g., using cloud storage). This example provides a starting point. You'll need to research the specific MCP specifications and adapt the code to your particular model and requirements. Remember to consult the official MCP documentation and any relevant libraries for more detailed information.

Paradex Server

A Model Context Protocol server implementation that enables AI assistants to interact with the Paradex perpetual futures trading platform, allowing for retrieving market data, managing trading accounts, placing orders, and monitoring positions.

Axiom MCP Server

Una implementación de servidor de Protocolo de Contexto de Modelo para Axiom que permite a los agentes de IA consultar tus datos utilizando el Lenguaje de Procesamiento de Axiom (APL).

GitHub MCP Bridge

A Model Context Protocol server that enables AI agents to securely access and interact with GitHub Enterprise data, providing access to enterprise users, organizations, emails, and license information.

MCP

Servidor MCP

HubSpot CMS MCP Server

An auto-generated Multi-Agent Conversation Protocol Server for interacting with HubSpot CMS API, allowing AI agents to manage HubSpot content management system through natural language commands.

YouTube Data API MCP Server

A FastAPI server that enables interaction with YouTube's data through search, video details, channel information, and comment retrieval endpoints.

MCP Search Analytics Server

A Model Context Protocol server that provides unified access to Google Analytics 4 and Google Search Console data through real-time analytics queries.

Flutter MCP Server

A TypeScript-based MCP server that implements a simple notes system, enabling users to manage text notes with creation and summarization functionalities through structured prompts.

Waldzell Metagames Server

Provides access to 27+ structured problem-solving frameworks and game-theoretic workflows for software development, project management, and operations research. Helps prevent analysis paralysis and scope creep by transforming open-ended challenges into systematic, time-boxed approaches with clear decision gates.

inked

inked

arXiv Search MCP Server

Un servidor MCP que proporciona herramientas para buscar y obtener artículos de arXiv.org.

Basic MCP

A simple MCP server built with FastMCP for experimentation and learning purposes. Includes basic web tools like article fetching and serves as a human-readable template for building custom MCP servers.

Google Search Console MCP Server

Se conecta directamente a tu cuenta de Google Search Console a través de la API oficial, permitiéndote acceder a datos clave directamente desde herramientas de IA como Claude Desktop o OpenAI Agents SDK y otras.

Tinderbox MCP Server

Un servidor MCP para interactuar con la herramienta de gestión del conocimiento Tinderbox.

Dify MCP Server

Una implementación de servidor que permite la integración de flujos de trabajo de Dify con el Protocolo de Contexto de Modelo (MCP), permitiendo a los usuarios acceder a las capacidades de Dify a través de clientes compatibles con MCP.