Discover Awesome MCP Servers

Gradescope MCP Server

An MCP server that enables AI assistants to interact with Gradescope for course management, grading workflows, and regrade reviews. It provides instructors and TAs with tools for assignment management, individual or batch grading, and rubric manipulation.

MCP Integration Server

Enables agents to interact with Salesforce, Atlassian (Jira/Confluence), and Supabase through standardized tools and resources. Provides both read-only access for querying data and write operations for managing records across these enterprise platforms.

AlibabaCloud DevOps MCP Server

Enables AI assistants to interact with Alibaba Cloud Yunxiao platform for managing code repositories, work items, pipelines, packages, and application delivery. Supports project collaboration, code reviews, and automated deployment workflows.

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.

MCP Local LLM Server

A privacy-first MCP server that provides local LLM-enhanced tools for code analysis, security scanning, and automated task execution using backends like Ollama and LM Studio. It enables symbol-aware code reviews and workspace exploration while ensuring that all code and analysis remain strictly on your local machine.

qring-mcp

A quantum-inspired secret manager that anchors API keys to your OS-native vault, preventing plaintext .env leaks. It empowers AI agents with advanced mechanics like multi-environment superposition, linked entanglements, and ephemeral in-memory tunneling.

inked

inked

Logic-Thinking MCP Server

Enables formal logical reasoning, mathematical problem-solving, and proof construction across 11 logic systems including propositional, predicate, modal, fuzzy, and probabilistic logic. Integrates external solvers (Z3, ProbLog, Clingo) for advanced reasoning, with support for proof storage, argument scoring, and cross-system translation.

arXiv Search MCP Server

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

Cursor Auto-Review MCP Server

An MCP server that automates code reviews through linting, testing, and git diff analysis. It also generates conventional commit messages and detailed pull request descriptions based on file changes and code patterns.

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.

MCP Fullstack Application

A complete fullstack application that demonstrates Model Context Protocol integration for AI assistants to securely connect to external data sources and tools, providing task management functionality with a Convex backend and Remix frontend.

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.

Spring Boot AI MCP Client

Here are a few options for translating the English phrase "A Spring Boot application for interacting with MCP servers using Spring AI Chat Client and Rest Controller" into Spanish, with slight variations in nuance: **Option 1 (Most Direct):** > Una aplicación Spring Boot para interactuar con servidores MCP utilizando Spring AI Chat Client y un Rest Controller. This is the most literal translation and is perfectly understandable. It keeps the English terms "Spring AI Chat Client" and "Rest Controller" as they are commonly used in Spanish-speaking development communities. **Option 2 (Slightly More Natural):** > Una aplicación Spring Boot para la interacción con servidores MCP, utilizando Spring AI Chat Client y un controlador Rest. This version uses "la interacción" instead of "interactuar," which can sound slightly more natural in Spanish. It also translates "Rest Controller" to "controlador Rest." **Option 3 (Emphasizing the Purpose):** > Una aplicación Spring Boot diseñada para interactuar con servidores MCP, empleando Spring AI Chat Client y un Rest Controller. This option uses "diseñada para" (designed for) and "empleando" (employing) to emphasize the application's purpose and the tools it uses. **Option 4 (More Technical):** > Una aplicación Spring Boot para la comunicación con servidores MCP, implementando Spring AI Chat Client y un Rest Controller. This option uses "comunicación" (communication) and "implementando" (implementing), which might be preferred in a more technical context. **Recommendation:** I would recommend **Option 1** or **Option 2** as the most generally applicable and easily understood translations. The choice between them depends on whether you prefer the verb "interactuar" or the noun "la interacción." If you're writing for a highly technical audience, Option 4 might be suitable.

Stock Data MCP Server

Enables querying financial data including stocks, indices, funds, and futures from Chinese, Hong Kong, and US markets. Provides real-time market information, financial indicators, news, and trading suggestions through Eastmoney and Sina data sources.

Link Scan MCP Server

Automatically scans and summarizes video links (YouTube, Instagram Reels) and text links (blogs, articles) using AI-powered transcription and summarization. Provides concise 3-sentence summaries without requiring API keys.

YouTube Comments MCP Server

Enables fetching and analyzing YouTube video comments and replies through the YouTube Data API. Supports sorting by relevance or time and returns structured JSON data for comment analysis, summarization, and translation tasks.

Wassenger WhatsApp MCP Server

Enables AI assistants to send messages, analyze conversations, manage chats and groups, schedule messages, and automate WhatsApp business operations through the Wassenger API using natural language commands.

Paylocity MCP Server

Connects Claude Desktop to the Paylocity API to manage employee records, pay statements, and company headcount data through natural language. It includes automated data protection that redacts sensitive information like SSNs and bank account numbers before reaching the model.

Xiaohongshu (XHS) Creator Toolkit

An automation toolkit that enables AI-driven content publishing and creator data analysis for Xiaohongshu via the MCP protocol. It supports automated posting of image and video notes, cookie management, and performance metric tracking through natural language conversations.

GitLab MCP Server

Enables AI assistants to interact with GitLab projects through natural language, allowing users to query merge requests, view code reviews, check test results and pipelines, and respond to discussions directly from chat.

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.

Boxtalk Data MCP Server

Provides tools for interacting with SQL Server databases, enabling users to retrieve paginated table data, inspect schemas, and count records. It features enforced pagination and secure configuration options to manage database operations efficiently through natural language.

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.

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.

Nucleus MCP

Nucleus MCP provides a local, unified memory layer that synchronizes context and decisions across different AI tools like Cursor, Claude Desktop, and Windsurf. It enables cross-platform state management and persistent knowledge storage using a shared local brain.

lyxamour-mcp

A comprehensive Python MCP server with built-in knowledge base (SQLite + FTS5), web management interface, and flexible tool grouping system. Supports multiple transport protocols (stdio, SSE, HTTP Stream) with zero external dependencies.