Discover Awesome MCP Servers

MCP Web Docs

A self-hosted MCP server that crawls, indexes, and searches documentation from any website locally, including private sites requiring authentication. It provides hybrid search capabilities and local embedding generation to maintain privacy while keeping AI assistant knowledge up to date.

Kollektiv

Kollektiv

TheHive MCP Server

TheHive MCP Server

MCP2Brave

Un servidor basado en el protocolo MCP que utiliza la API de Brave para la funcionalidad de búsqueda web.

Laravel Forge MCP Server

A minimalist MCP server that integrates with Laravel Forge, allowing users to manage their Laravel Forge servers, sites, and deployments through AI assistants like Claude Desktop, Windsurf, or Cursor.

facebook-mcp-server

facebook-mcp-server

Remote MCP Server with Bearer Auth

A Cloudflare Workers-based MCP server implementation that supports OAuth/bearer token authentication, enabling secure remote interaction with Model Context Protocol tools.

WhatsApp MCP Server

Implementación de un servidor MCP (Protocolo de Control de Mensajes) de WhatsApp.

aifais-mcp-server

Headless document processing for AI agents. Invoice extraction, contract analysis, and Dutch business verification. Pay-per-use via X402 on Solana. No API keys needed.

Knowledge Graph Builder

Transforms text or web content into structured knowledge graphs using local AI models with MCP integration for persistent storage in Neo4j and Qdrant.

MCP Notion Server (@suncreation)

An MCP server that enables LLMs to interact with Notion workspaces via the Notion API, supporting page creation, database management, and content retrieval. It features markdown conversion to optimize token usage and enhanced error handling for more reliable workspace interactions.

MCP Template

A template MCP server built with FastMCP framework that demonstrates basic tool implementation with a simple addition calculator example.

Google Calendar MCP Server

Servidor de Protocolo de Contexto del Modelo (MCP) que se integra con la API de Google Calendar.

KaiaFun MCP

Un servidor MCP que permite el listado de tokens, el comercio y la interacción con la blockchain de Kaia a través de Claude Desktop.

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.

streamable-mcp-server

A TypeScript boilerplate for building Model Context Protocol servers using the Streamable HTTP transport and session management. It serves as a foundational template with pre-configured development tools to help developers quickly build and deploy streamable MCP services.

MCP Salesforce Revenue Cloud

Provides AI assistants with direct access to Salesforce Revenue Cloud data and operations, enabling retrieval of products, price books, quotes, orders, and execution of custom SOQL queries through natural language.

PDF-Tools MCP Server

Enables PDF generation from HTML, text, and Markdown content with customizable formatting options. Provides secure cross-platform PDF creation tools that automatically save to user directories like Downloads, Documents, or Desktop.

MCP Server for n8n Integration

A Model Context Protocol server that enables AI agents to interact with n8n workflows and automation tools through a standardized interface, allowing execution of workflows and access to n8n functions.

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.

Dynamic MCP UI Generator

A sophisticated server that enables users to create, customize, and generate interactive UI components with features like dynamic form building, dashboard creation, and chart generation through a modern glassmorphism interface.

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 Agent Platform

Un sistema de interacción persona-ordenador multiagente que permite la interacción natural a través de capacidades integradas de reconocimiento visual, reconocimiento de voz y síntesis de voz.

n8n MCP Server

Enables full workflow automation management in n8n through 40+ tools covering workflows, executions, credentials, tags, variables, projects, users, and source control operations.

LlamaCloud MCP Server

Un servidor MCP local que se integra con Claude Desktop, habilitando capacidades RAG para proporcionar a Claude información privada y actualizada desde índices LlamaCloud personalizados.

MCP Unity Editor

Enables AI assistants to interact with Unity Editor through the Model Context Protocol, allowing natural language control of Unity projects including scene manipulation, GameObject creation, component updates, package management, and test execution.

Kakao Bot MCP Server

An implementation of the Model Context Protocol that connects AI agents to Kakao Official Accounts, allowing users to send various message templates through the Kakao Developers API.