Discover Awesome MCP Servers

Paradex Server

AIアシスタントがParadexの無期限先物取引プラットフォームと連携し、市場データの取得、取引口座の管理、注文の発注、ポジションの監視を可能にする、モデルコンテキストプロトコルサーバーの実装。

ClickFunnels MCP Framework

ClickFunnelsとClaude Desktopを統合するモデルコンテキストプロトコルサーバー。ユーザーは自然言語を通じて、ClickFunnelsアカウントからファネルやコンタクトをリスト表示したり、取得したりできます。

MCP2Brave

Brave API を使用してウェブ検索機能を提供する、MCP プロトコルに基づいたサーバー。

GitLab MCP Server

Connects AI assistants to GitLab, enabling natural language queries for merge requests, reviews, discussions, pipeline tests, and job logs with the ability to respond to comments and resolve discussions.

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.

Geolocation MCP Server

Provides geolocation services including finding nearby transit stops and retrieving WalkScore, TransitScore, and BikeScore ratings for any address or coordinates using the WalkScore API.

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

モデルコンテキストプロトコル (MCP) サーバー (Google Calendar API と統合)

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. This will be a simplified example to get you started. Keep in mind that a full MCP implementation can be quite complex depending on the features you want to support. **Conceptual Overview** The Model Context Protocol (MCP) is a protocol that allows a client (e.g., a code editor, IDE, or other tool) to interact with a language server or other process that provides information about a code model. It's similar in spirit to the Language Server Protocol (LSP), but often focuses on more general model information rather than just language-specific features. **Simplified Example Structure** For this Playground example, we'll create a very basic server that responds to a single request: 1. **Client Request:** The client sends a request to the server asking for information about a specific "model element" (e.g., a variable, function, class). We'll represent this with a simple string identifier. 2. **Server Processing:** The server receives the request, looks up information about the model element (in our case, from a hardcoded dictionary), and sends a response back to the client. 3. **Client Response:** The client receives the response and displays the information. **Playground Code (Swift)** ```swift import Foundation // 1. Define the data structures struct MCPRequest: Codable { let elementId: String } struct MCPResponse: Codable { let elementId: String let description: String } // 2. Simulated Model Data (Replace with your actual model) let modelData: [String: String] = [ "myVariable": "This is a variable that stores an integer.", "myFunction": "This is a function that calculates the square of a number.", "MyClass": "This is a class that represents a user.", "anotherVariable": "This is another variable used for counting." ] // 3. Server Logic (Simplified) func handleRequest(requestData: Data) -> Data? { do { let decoder = JSONDecoder() let request = try decoder.decode(MCPRequest.self, from: requestData) guard let description = modelData[request.elementId] else { print("Element not found: \(request.elementId)") return nil // Or return an error response } let response = MCPResponse(elementId: request.elementId, description: description) let encoder = JSONEncoder() return try encoder.encode(response) } catch { print("Error decoding/encoding: \(error)") return nil // Or return an error response } } // 4. Simulate Client-Server Communication (in the same Playground) // Simulate a client request let clientRequest = MCPRequest(elementId: "myFunction") do { let encoder = JSONEncoder() let requestData = try encoder.encode(clientRequest) // Simulate sending the request to the server (in reality, this would be over a network connection) if let responseData = handleRequest(requestData: requestData) { let decoder = JSONDecoder() let response = try decoder.decode(MCPResponse.self, from: responseData) print("Received response for element: \(response.elementId)") print("Description: \(response.description)") } else { print("No response received or error occurred.") } } catch { print("Error creating request: \(error)") } // Example of a request for a non-existent element let clientRequest2 = MCPRequest(elementId: "nonExistentElement") do { let encoder = JSONEncoder() let requestData = try encoder.encode(clientRequest2) // Simulate sending the request to the server (in reality, this would be over a network connection) if let responseData = handleRequest(requestData: requestData) { let decoder = JSONDecoder() let response = try decoder.decode(MCPResponse.self, from: responseData) print("Received response for element: \(response.elementId)") print("Description: \(response.description)") } else { print("No response received or error occurred for non-existent element.") } } catch { print("Error creating request: \(error)") } ``` **Explanation:** 1. **Data Structures:** `MCPRequest` and `MCPResponse` define the structure of the messages exchanged between the client and server. They use `Codable` to make it easy to serialize and deserialize them to/from JSON. 2. **Model Data:** `modelData` is a dictionary that simulates your code model. In a real application, this would be replaced with a more sophisticated representation of your code. 3. **`handleRequest` Function:** This function is the core of the server. It receives the request data, decodes it, looks up the information in `modelData`, creates a response, encodes it, and returns the response data. Error handling is included. 4. **Client Simulation:** The code simulates a client sending a request to the server and receiving the response. In a real application, this would involve network communication (e.g., using sockets or a framework like `NIO`). **How to Run:** 1. Open Xcode. 2. Create a new Playground (File -> New -> Playground). 3. Copy and paste the code into the Playground. 4. Run the Playground (Shift + Command + Enter). **Important Considerations and Next Steps:** * **Network Communication:** This example uses in-memory communication. To create a real server, you'll need to use sockets or another networking mechanism to allow clients to connect to the server. Consider using SwiftNIO for asynchronous network handling. * **Error Handling:** The error handling in this example is basic. You should implement more robust error handling, including sending error responses to the client. * **Model Representation:** The `modelData` dictionary is a very simple representation of a code model. In a real application, you'll need a more sophisticated data structure to represent the code, including information about types, scopes, relationships between elements, etc. Consider using a graph database or a custom data structure. * **Request Types:** This example only handles one type of request. You'll need to define different request types for different operations (e.g., getting the type of a variable, finding all references to a function, etc.). * **Asynchronous Operations:** Many operations in a language server can be time-consuming (e.g., parsing code, performing static analysis). You should use asynchronous operations to avoid blocking the server's main thread. * **Concurrency:** If you want to handle multiple client requests concurrently, you'll need to use concurrency mechanisms (e.g., threads, dispatch queues, actors). * **JSON RPC:** MCP often uses JSON RPC as the underlying communication protocol. Consider using a library that provides JSON RPC support. * **Real-World MCP Implementations:** Look at existing MCP implementations (if any are publicly available) for inspiration. The Language Server Protocol (LSP) is a good starting point, as MCP is often inspired by it. **Example using SwiftNIO (Illustrative - Requires Setup)** This is a very basic example to show how you *might* use SwiftNIO. You'll need to add SwiftNIO as a dependency to your project (using Swift Package Manager). This code *won't* run directly in a Playground without extra setup. ```swift // **This is illustrative and requires SwiftNIO setup** import NIO import NIOFoundationCompat // ... (MCPRequest, MCPResponse, modelData, handleRequest from above) class MCPHandler: ChannelInboundHandler { typealias InboundIn = ByteBuffer typealias OutboundOut = ByteBuffer func channelRead(context: ChannelHandlerContext, data: NIOAny) { var buffer = self.unwrapInboundIn(data) let requestData = Data(buffer: buffer) if let responseData = handleRequest(requestData: requestData) { var responseBuffer = context.channel.allocator.buffer(capacity: responseData.count) responseBuffer.writeBytes(responseData) context.writeAndFlush(self.wrapOutboundOut(responseBuffer), promise: nil) } else { // Handle error (e.g., send an error response) print("Error handling request") } } func channelReadComplete(context: ChannelHandlerContext) { context.flush() } func errorCaught(context: ChannelHandlerContext, error: Error) { print("error: ", error) context.close(promise: nil) } } let group = MultiThreadedEventLoopGroup(numberOfThreads: 1) // Or more threads let bootstrap = ServerBootstrap(group: group) // Specify backlog and enable SO_REUSEADDR for the server itself .serverChannelOption(ChannelOptions.backlog, value: 256) .serverChannelOption(ChannelOptions.socket(SocketOptionLevel(SOL_SOCKET), SO_REUSEADDR), value: 1) // Set the handlers that are applied to the accepted Channels .childChannelInitializer { channel in channel.pipeline.addHandler(MCPHandler()) } // Enable SO_REUSEADDR for the accepted Channels .childChannelOption(ChannelOptions.socket(SocketOptionLevel(SOL_SOCKET), SO_REUSEADDR), value: 1) .childChannelOption(ChannelOptions.maxMessagesPerRead, value: 16) .childChannelOption(ChannelOptions.recvAllocator, value: AdaptiveRecvByteBufferAllocator()) defer { try! group.syncShutdownGracefully() } do { let channel = try bootstrap.bind(host: "localhost", port: 8888).wait() print("Server started and listening on \(channel.localAddress!)") // This will block. try channel.closeFuture.wait() } catch { fatalError("Failed to start server: \(error)") } ``` **Explanation of SwiftNIO Example:** 1. **`MCPHandler`:** This is a `ChannelInboundHandler` that receives data from the client, processes it using the `handleRequest` function, and sends the response back to the client. 2. **`ServerBootstrap`:** This sets up the server to listen for incoming connections. 3. **Event Loop Group:** SwiftNIO uses an event loop group to handle asynchronous operations. 4. **Binding:** The `bind` function starts the server listening on a specific host and port. **To use the SwiftNIO example:** 1. Create a new Xcode project (not a Playground). 2. Add SwiftNIO as a dependency using Swift Package Manager (File -> Add Packages...). 3. Copy and paste the code into your project. 4. Run the project. You would then need to write a client application (also using SwiftNIO or another networking library) to connect to the server and send requests. This is a complex topic, and this is just a starting point. Good luck! Remember to break down the problem into smaller, manageable steps.

MySQL MCP Server

Enables read-only access to MySQL databases through natural language queries. Provides automatic table schema discovery and executes SELECT, SHOW, DESCRIBE, and EXPLAIN statements within secure read-only transactions.

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.

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.

Axiom MCP Server

Axiom のためのモデルコンテキストプロトコルサーバー実装。これにより、AIエージェントは Axiom Processing Language (APL) を使用してあなたのデータをクエリできます。

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.

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.

MCP

MCPサーバー (MCP Sābā)

Fetch MCP Server

シンプルなAPI呼び出しを通じて、様々な形式（HTML、JSON、プレーンテキスト、Markdown）のウェブコンテンツを取得・変換する機能を提供します。

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.

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.

Remote MCP Server (Authless)

A template for deploying authentication-free MCP servers on Cloudflare Workers. Allows quick deployment and connection to MCP clients like Claude Desktop or Cloudflare AI Playground via Server-Sent Events.

Limitless MCP Server

An MCP server that connects Limitless Pendant wearable data to AI tools like Claude and Windsurf, allowing AI assistants to interact with your personal Lifelog recordings through structured tools and searches.

Google Search Console MCP Server

これは、公式APIを通じてGoogle Search Consoleアカウントに直接接続し、Claude DesktopやOpenAI Agents SDKなどのAIツールから主要なデータに直接アクセスできるようにするものです。

Math-Physics-ML MCP System

Provides GPU-accelerated scientific computing capabilities including symbolic mathematics, quantum wave mechanics simulations, molecular dynamics, and neural network training through four specialized MCP servers.