Discover Awesome MCP Servers

japan-ux-mcp

Provides Japanese UX conventions as an MCP server to help AI generate correct Japanese UI elements like proper name order, furigana, phone formats, and polite language. It includes tools for form generation, validation, keigo suggestions, and cultural adaptation for developers building Japanese-facing products.

Clarify MCP Server

Enables AI agents to ask clarification questions and receive structured user input through a Human-in-the-Loop interface.

Asta MCP Servers

Provides MCP wrappers for Allen AI's Asta research tools, enabling AI agents to manage local document collections and perform deep literature searches. It supports automated report generation, PDF text extraction, and research hypothesis synthesis.

PowerShell Exec MCP Server

Enables secure PowerShell command execution with specialized script generation for Microsoft Intune and IBM BigFix management platforms, including detection/remediation scripts, system monitoring, and enterprise automation.

context-overflow-mcp

Integrates the Context Overflow Q\&A platform with Claude Code, enabling question posting, answering, voting, and platform health monitoring.

suggest-skills

An MCP server that generates skill manifests from GitHub skills directories and provides tools to recommend and download AI agent skills.

@langapi/mcp-server

MCP server for AI-powered translation management in i18n projects, enabling automated locale detection, translation status checks, and sync via LangAPI.

vercel-mcp-pro

A comprehensive MCP server with 70 tools covering the entire Vercel REST API, enabling management of deployments, projects, environment variables, domains, DNS, aliases, certificates, logs, checks, webhooks, edge config, and teams via a Vercel access token.

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.

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.

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.

kreports

Enables investors and analysts to query Korean listed companies' financial health, accounting risks, and disclosure events using DART filings, accessible via natural language through Claude.

hlido-mcp

Independent trust scores, claim audits, and side-by-side comparisons for AI agents, queryable over MCP. Every verdict is backed by hands-on testing and signed evidence from Hlido (hlido.eu). Hosted endpoint available at https://hlido.eu/mcp — no auth required.

@yawlabs/caddy-mcp

Manage Caddy web servers from Claude Code, Cursor, and any MCP client with 18 tools + 4 resources covering every endpoint of Caddy's admin API — config, routes, reverse proxies, TLS, PKI, metrics, snapshots.

Fetch MCP Server

Proporciona funcionalidad para obtener y transformar contenido web en varios formatos (HTML, JSON, texto plano y Markdown) a través de simples llamadas a la API.

inked

inked

stock-analyzer-ai-mcp

Analyze stocks with financial ratios, comparisons, and sector performance data.

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.

Odoo 19 MCP Server

Provides tools to interact with Odoo 19's External JSON-2 API, enabling CRUD operations and complex queries on Odoo databases with multi-company support.

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.

protonmail-mcp

Enables AI clients to interact with ProtonMail accounts through the Proton Bridge using SMTP and IMAP protocols. Provides email management capabilities via secure local bridge connections.

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.

UCO Bank MCP Server

Enables interaction with UCO Bank APIs through the Model Context Protocol, supporting banking operations such as account lookup, transaction history, and more.

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.

neurodivergent-memory

MCP server for knowledge graphs designed around neurodivergent thinking patterns, organizing memories into five districts with BM25 ranking and bidirectional connections.

Google Tasks MCP Server

A Model Context Protocol server that enables AI assistants to manage Google Tasks, including creating, updating, deleting, and searching tasks and task lists via OAuth2 authentication.