Discover Awesome MCP Servers

Torify — Japanese Locale APIs for AI Agents

Torify gives AI agents the Japanese locale primitives that standard libraries lack — imperial era date conversion (wareki), qualified invoice number validation with NTA registry lookup, corporate number lookup (法人番号), postal code resolution, name romanization (Hepburn), and kanji-to-kana conversion via Yahoo! JLP. 31 endpoints total. No authentication required for MCP. Pay-per-call $0.02/call via

MCP-Discord

Enables AI assistants to interact with Discord servers through a bot, supporting channel management, messaging, forum operations, reactions, and webhooks.

MCP Document Indexer

Enables real-time indexing and semantic search of local documents (PDF, Word, text, Markdown, RTF) using vector embeddings and local LLMs. Monitors folders for changes and provides natural language search capabilities through Claude Desktop integration.

alaya

Enables Claude Code to serve as the primary interface for a personal knowledge vault (zk or Obsidian), allowing full read, write, search, and synthesis operations on notes.

Terminal Controller MCP Server

Enables secure terminal command execution, directory navigation, and file system operations through a standardized interface.

Flight Finder MCP

Enables Claude to search and compare flights across multiple providers (Skyscanner, Google Flights, Kiwi.com) with smart caching, parallel queries, and flexible filtering.

CI MCP Server

Enables AI assistants to manage SAP Cloud Integration (CPI) landscapes through natural language by exposing CPI OData APIs as MCP tools.

Refero MCP

Enables searching the Refero design catalog in plain English and generates DESIGN.md files for any project.

superposition-mcp

Provides a deterministic, keyless two-pole terrain map from an agent's task framings to counter premature collapse in reasoning, working offline or via API.

remote-mcp-server

Deploys a remote MCP server on Cloudflare Workers with OAuth login, enabling tool calls via SSE from clients like Claude Desktop.

Reality Calendar MCP Server

Enables interaction with tool data stored in Google Drive Excel files through cached SQLite database. Provides access to tool information and descriptions with automatic background synchronization and OpenWebUI compatibility via OpenAI proxy.

Model Context Protocol (MCP) Server

A Python implementation of the MCP server that enables AI models to connect with external tools and data sources through a standardized protocol, supporting tool invocation and resource access via JSON-RPC.

mcp-api-tests

Starter scaffold for a workshop to build an MCP API testing server, intended to be extended through incremental steps.

defined-mcp

MCP server for managing Defined Networking infrastructure through API tools. It enables network administration including host management, firewall rules, tags, and network configuration with Claude Code integration for interactive network design and auditing.

Joplin MCP Server

Enables AI assistants to interact with Joplin notes and notebooks, including searching, reading, and listing notebooks through natural language commands via the MCP protocol.

Web Research MCP

Enables AI agents to search the web, fetch pages, and synthesize research through three tools: web_search, fetch_page, and research_topic, all in a single pay-per-use API call.

mcp-ai-voice

Enables AI agents to synthesize natural speech using either platform system voices or premium OpenAI TTS, with automatic engine selection and graceful fallback.

Better Prompts MCP

Automatically extracts actionable methodologies from articles and URLs, stores them in a vector database, and retrieves relevant methods to enhance user prompts for more effective AI interactions.

tasksync-mcp

MCP server to give new instructions to agent while its working. It uses the get_feedback tool to collect your input from the feedback.md file in the workspace, which is sent back to the agent when you save.

Stamp it

An MCP server that adds full-screen text or image watermarks to images with intelligent color adaptation and multi-language support.

visualcrossing

Provides access to Visual Crossing Weather Timeline API for weather data retrieval via natural language queries.

MATLAB MCP Server

A Model Context Protocol (MCP) server that enables seamless integration between MATLAB and MCP-compatible applications like Claude Code. Execute MATLAB code, manage workspace variables, create plots, handle data I/O, and more - all through a token-efficient MCP interface.

MCP Think

A Model Context Protocol server that provides AI assistants like Claude with a dedicated space for structured thinking during complex problem-solving tasks.

Bocha Search MCP

Um motor de busca focado em IA que permite que aplicações de IA acessem conhecimento de alta qualidade de bilhões de páginas da web e fontes de conteúdo do ecossistema em vários domínios, incluindo clima, notícias, enciclopédia, informações médicas, passagens de trem e imagens.

Accounting MCP Server

Enables personal financial management through AI assistants by providing tools to add transactions, check balances, list transaction history, and generate monthly summaries. Supports natural language interaction for tracking income and expenses with categorization.

Build

Okay, I can help you understand how to use the TypeScript SDK to create different MCP (Model Configuration Protocol) servers. However, I need a little more information to give you the *most* helpful and specific answer. Please tell me: 1. **What MCP server are you trying to create?** Are you trying to create a custom MCP server for a specific game, or are you trying to create a generic MCP server? 2. **What TypeScript SDK are you referring to?** There are many TypeScript SDKs that could be used to create an MCP server. Please provide the name of the SDK or a link to the documentation. 3. **What functionality do you need?** What specific features do you need your MCP server to support? For example, do you need to support authentication, authorization, or data validation? In the meantime, here's a general outline of how you might approach creating an MCP server using TypeScript, assuming you're building a custom server and not using a pre-built SDK (which would have its own specific instructions): **General Approach (Custom Implementation):** 1. **Project Setup:** * Initialize a new TypeScript project: ```bash mkdir my-mcp-server cd my-mcp-server npm init -y npm install typescript --save-dev npm install express body-parser cors --save # Common dependencies for a web server npm install --save-dev @types/node @types/express @types/body-parser @types/cors # Type definitions npx tsc --init # Initialize tsconfig.json ``` * Configure `tsconfig.json`: Adjust settings like `target`, `module`, `outDir`, `rootDir`, `esModuleInterop`, and `strict` to suit your project's needs. A basic example: ```json { "compilerOptions": { "target": "es6", "module": "commonjs", "outDir": "./dist", "rootDir": "./src", "strict": true, "esModuleInterop": true, "skipLibCheck": true, "forceConsistentCasingInFileNames": true }, "include": ["src/**/*"], "exclude": ["node_modules"] } ``` 2. **Define Data Structures (Interfaces/Types):** * Create TypeScript interfaces or types to represent the data structures used in the MCP protocol. This will depend entirely on the specific MCP protocol you're implementing. For example: ```typescript // src/types/mcp.ts export interface ModelConfiguration { modelId: string; version: number; parameters: { [key: string]: any }; } export interface MCPRequest { requestId: string; action: "get" | "set" | "delete"; modelId?: string; configuration?: ModelConfiguration; } export interface MCPResponse { requestId: string; status: "success" | "error"; data?: ModelConfiguration | null; error?: string; } ``` 3. **Implement the Server (using Express.js):** * Create an Express.js server to handle MCP requests. ```typescript // src/index.ts import express, { Request, Response } from 'express'; import bodyParser from 'body-parser'; import cors from 'cors'; import { MCPRequest, MCPResponse, ModelConfiguration } from './types/mcp'; const app = express(); const port = 3000; app.use(cors()); app.use(bodyParser.json()); // In-memory storage (replace with a database in a real application) const modelConfigurations: { [modelId: string]: ModelConfiguration } = {}; app.post('/mcp', (req: Request, res: Response) => { const mcpRequest: MCPRequest = req.body; console.log("Received MCP Request:", mcpRequest); switch (mcpRequest.action) { case "get": if (!mcpRequest.modelId) { sendErrorResponse(res, mcpRequest.requestId, "Model ID is required for 'get' action."); return; } const config = modelConfigurations[mcpRequest.modelId]; if (config) { sendSuccessResponse(res, mcpRequest.requestId, config); } else { sendSuccessResponse(res, mcpRequest.requestId, null); // Model not found } break; case "set": if (!mcpRequest.configuration) { sendErrorResponse(res, mcpRequest.requestId, "Configuration is required for 'set' action."); return; } if (!mcpRequest.configuration.modelId) { sendErrorResponse(res, mcpRequest.requestId, "Model ID is required in the configuration for 'set' action."); return; } modelConfigurations[mcpRequest.configuration.modelId] = mcpRequest.configuration; sendSuccessResponse(res, mcpRequest.requestId, mcpRequest.configuration); break; case "delete": if (!mcpRequest.modelId) { sendErrorResponse(res, mcpRequest.requestId, "Model ID is required for 'delete' action."); return; } delete modelConfigurations[mcpRequest.modelId]; sendSuccessResponse(res, mcpRequest.requestId, null); break; default: sendErrorResponse(res, mcpRequest.requestId, "Invalid action."); } }); function sendSuccessResponse(res: Response, requestId: string, data: ModelConfiguration | null) { const response: MCPResponse = { requestId: requestId, status: "success", data: data, }; res.json(response); } function sendErrorResponse(res: Response, requestId: string, errorMessage: string) { const response: MCPResponse = { requestId: requestId, status: "error", error: errorMessage, }; res.status(400).json(response); } app.listen(port, () => { console.log(`MCP Server listening at http://localhost:${port}`); }); ``` 4. **Implement MCP Logic:** * Implement the core logic for handling MCP requests. This will involve: * Parsing the request. * Validating the request. * Retrieving, updating, or deleting model configurations. * Constructing the response. 5. **Data Storage:** * Choose a data storage mechanism to store model configurations. This could be: * In-memory storage (for simple prototypes). * A file-based database (e.g., SQLite). * A relational database (e.g., PostgreSQL, MySQL). * A NoSQL database (e.g., MongoDB, Redis). 6. **Error Handling:** * Implement robust error handling to gracefully handle invalid requests, data validation errors, and other potential issues. 7. **Authentication and Authorization (if needed):** * If your MCP server needs to be secure, implement authentication and authorization mechanisms to control access to model configurations. 8. **Build and Run:** * Compile the TypeScript code: `npm run build` (or `tsc` if you haven't configured a build script). * Run the server: `node dist/index.js` **Example Request (using `curl`):** ```bash curl -X POST -H "Content-Type: application/json" -d '{ "requestId": "123", "action": "set", "configuration": { "modelId": "myModel", "version": 1, "parameters": { "param1": "value1", "param2": 123 } } }' http://localhost:3000/mcp ``` **Important Considerations:** * **Security:** If your MCP server will be exposed to a network, security is paramount. Use HTTPS, implement authentication and authorization, and carefully validate all input. * **Scalability:** If you anticipate a high volume of requests, consider using a scalable architecture, such as a load balancer and multiple server instances. * **Monitoring:** Implement monitoring to track the health and performance of your MCP server. * **Testing:** Write unit tests and integration tests to ensure that your MCP server is working correctly. **Example `package.json` (with build script):** ```json { "name": "my-mcp-server", "version": "1.0.0", "description": "", "main": "index.js", "scripts": { "build": "tsc", "start": "node dist/index.js", "dev": "ts-node-dev --respawn src/index.ts" }, "keywords": [], "author": "", "license": "ISC", "dependencies": { "body-parser": "^1.20.2", "cors": "^2.8.5", "express": "^4.18.2" }, "devDependencies": { "@types/body-parser": "^1.19.5", "@types/cors": "^2.8.17", "@types/express": "^4.17.21", "@types/node": "^20.10.5", "ts-node-dev": "^2.0.0", "typescript": "^5.3.3" } } ``` **To run this example:** 1. Save the code into the appropriate files (e.g., `src/index.ts`, `src/types/mcp.ts`). 2. Run `npm install` to install dependencies. 3. Run `npm run build` to compile the TypeScript code. 4. Run `npm start` to start the server. **Next Steps:** Provide more details about the specific MCP server you're trying to create, the SDK you're using, and the functionality you need, and I can give you more tailored guidance.

au-weather-mcp

Provides access to Australian weather data from the Bureau of Meteorology, enabling location search, forecasts, and current observations.

Iris MCP Server

A multi-backend gateway that enables access to various services like Google Drive and Notion through a single MCP connector. It currently provides comprehensive Google Drive integration for reading, writing, and managing files and folders.

Prompt Bookmarks

Enables users to organize, search, and manage a shared library of prompts across AI tools via the Model Context Protocol. It supports hierarchical folder organization, tagging, and template variable substitution for dynamic prompt generation.

Android Puppeteer

Enables AI agents to interact with Android devices through visual UI element detection and automated interactions. Provides comprehensive Android automation capabilities including touch gestures, text input, screenshots, and video recording via uiautomator2.