Discover Awesome MCP Servers

ContextAtlas

Enables AI coding agents to retrieve and manage code context with hybrid search, project memory, and observability via MCP tools.

warpline

Temporal/change-impact authority that tracks per-entity change history and downstream propagation queries, enabling agents to assess impact and re-verify work before claiming completion.

MCP Time Server

Provides time and date functionality with support for multiple formats and timezones, enabling users to get current time/date, format timestamps, and retrieve comprehensive datetime information.

Basecamp MCP Server

Enables Claude to interact with Basecamp API for project and to-do management via OAuth authentication.

Track-It Process Monitor

Enables Claude to monitor and inspect running processes through a lightweight wrapper that captures stdout/stderr logs and stores process metadata in SQLite. Users can track any command execution and query process status, logs, and history through natural language.

safe-ssh-mcp

A secure SSH MCP server that enables execution of read-only diagnostic commands over SSH, preventing modifications to remote systems.

figma-developer-docs-mcp

Provides AI assistants with structured access to complete Figma developer documentation, including Plugin, Widget, and REST APIs. It enables users to search and read over 600 documentation pages to facilitate Figma-related development tasks.

CryptoGuard

Per-transaction crypto trade validator for AI agents, validates trades with PROCEED/CAUTION/BLOCK verdicts, scans tokens for anomalies, detects rug pulls, and searches across DEXes. Powered by WaveGuard physics engine with deterministic PDE-based analysis.

Maiga API MCP Server

Provides comprehensive integration with the Maiga API for cryptocurrency analysis, including token technicals, social sentiment tracking, and KOL insights. It enables AI assistants to retrieve market reports, trending token data, and detailed on-chain information.

Remote MCP Server Authless

A simple way to deploy an authentication-free Model Context Protocol server on Cloudflare Workers that can be connected to AI tools like Claude Desktop or the Cloudflare AI Playground.

Polvenn MCP Server

A local-first MCP server for tracking beer releases on Vinmonopolet, enabling users to search new and upcoming beers, check store stock, find nearby stores, and manage watchlists.

ChatRPG

A lightweight ChatGPT app that converts your LLM into a Dungeon Master!

FastMCP Demo Server

A production-ready MCP server that provides hackathon resources and reusable starter prompts. Built with FastMCP framework and includes comprehensive deployment options for development and production environments.

mcp-units

MCP server for converting cooking measurements (volume, weight, temperature) between common units like ml, cup, g, oz, and Celsius/Fahrenheit.

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.

nano-banana-mcpv2

Enables AI image generation and editing using Google's Gemini Multimodal Image APIs.

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.

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.

Stamp it

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

Bocha Search MCP

一个以人工智能为中心的搜索引擎，使人工智能应用程序能够访问来自数十亿网页和生态系统内容源的高质量知识，涵盖各种领域，包括天气、新闻、百科全书、医疗信息、火车票和图像。

Build

Okay, I can help you understand how to use the TypeScript SDK to create different MCP (Mesh Configuration Protocol) servers. However, I need a little more context to give you the *most* helpful answer. Specifically, tell me: 1. **Which MCP SDK are you using?** There are several possibilities, including: * **Istio's MCP SDK (likely part of the `envoyproxy/go-control-plane` project, but you'd be using the TypeScript bindings).** This is the most common use case if you're working with Istio or Envoy. * **A custom MCP implementation.** If you're building your own MCP server from scratch, you'll need to define your own data structures and server logic. * **Another MCP SDK.** There might be other, less common, MCP SDKs available. 2. **What kind of MCP server do you want to create?** What specific resources will it serve? For example: * **Route Configuration (RDS) server:** Serves route configurations to Envoy proxies. * **Cluster Configuration (CDS) server:** Serves cluster definitions to Envoy proxies. * **Listener Configuration (LDS) server:** Serves listener configurations to Envoy proxies. * **Endpoint Discovery Service (EDS) server:** Serves endpoint information to Envoy proxies. * **A custom resource server:** Serves your own custom resource types. 3. **What is your desired level of detail?** Do you want: * **A high-level overview of the process?** * **Example code snippets?** * **A complete, runnable example?** (This would be more complex and require more information from you.) **General Steps (Assuming Istio/Envoy MCP):** Here's a general outline of the steps involved in creating an MCP server using a TypeScript SDK (assuming it's based on the Envoy/Istio MCP protocol): 1. **Install the Necessary Packages:** You'll need to install the appropriate TypeScript packages. This will likely involve: * The core gRPC library for TypeScript (`@grpc/grpc-js` or similar). * The generated TypeScript code from the Protocol Buffers (`.proto`) definitions for the MCP resources you want to serve (e.g., `envoy.config.route.v3`, `envoy.config.cluster.v3`, etc.). You'll typically use `protoc` (the Protocol Buffer compiler) and a TypeScript plugin to generate these files. * Potentially, a library that provides helper functions for working with MCP. ```bash npm install @grpc/grpc-js google-protobuf # And potentially other packages depending on your setup ``` 2. **Generate TypeScript Code from Protocol Buffers:** You'll need to obtain the `.proto` files that define the MCP resources (e.g., from the `envoyproxy/go-control-plane` repository or your own custom definitions). Then, use `protoc` to generate TypeScript code from these files. This will create the TypeScript classes that represent the resource types. Example `protoc` command (you'll need to adjust this based on your `.proto` file locations and plugin configuration): ```bash protoc --plugin=protoc-gen-ts=./node_modules/.bin/protoc-gen-ts --ts_out=. your_mcp_resource.proto ``` 3. **Implement the gRPC Service:** Create a TypeScript class that implements the gRPC service defined in the `.proto` files. This class will have methods that correspond to the MCP endpoints (e.g., `StreamRoutes`, `StreamClusters`, etc.). These methods will receive requests from Envoy proxies and return the appropriate resource configurations. 4. **Handle the MCP Stream:** The core of an MCP server is handling the bidirectional gRPC stream. Your service implementation will need to: * Receive `DiscoveryRequest` messages from the client (Envoy proxy). * Process the request, determining which resources the client is requesting. * Fetch the appropriate resource configurations from your data store (e.g., a database, a configuration file, or in-memory data). * Construct `DiscoveryResponse` messages containing the resource configurations. * Send the `DiscoveryResponse` messages back to the client. * Handle errors and stream termination gracefully. 5. **Manage Resource Versions (Important for Updates):** MCP uses versioning to ensure that clients receive consistent updates. You'll need to track the versions of your resources and include them in the `DiscoveryResponse` messages. When a client sends a `DiscoveryRequest`, it will include the version of the resources it currently has. Your server should only send updates if the client's version is out of date. 6. **Implement a Data Store (Configuration Source):** You'll need a way to store and manage the resource configurations that your MCP server serves. This could be a simple configuration file, a database, or a more complex configuration management system. 7. **Start the gRPC Server:** Use the gRPC library to start a gRPC server and register your service implementation with it. The server will listen for incoming connections from Envoy proxies. 8. **Configure Envoy to Use Your MCP Server:** Configure your Envoy proxies to connect to your MCP server. This will typically involve specifying the server's address and port in the Envoy configuration. **Example (Conceptual - Requires Adaptation):** ```typescript // Assuming you've generated TypeScript code from your .proto files // import { RouteDiscoveryServiceService, RouteDiscoveryServiceHandlers } from './route_discovery_grpc_pb'; // import { DiscoveryRequest, DiscoveryResponse } from './discovery_pb'; import * as grpc from '@grpc/grpc-js'; // Replace with your actual generated code interface DiscoveryRequest { versionInfo: string; node: any; // Replace with your Node type resourceNames: string[]; typeUrl: string; responseNonce: string; errorDetail: any; // Replace with your Status type } interface DiscoveryResponse { versionInfo: string; resources: any[]; // Replace with your Resource type typeUrl: string; nonce: string; controlPlane: any; // Replace with your ControlPlane type } interface RouteDiscoveryServiceHandlers { streamRoutes: grpc.ServerDuplexStream<DiscoveryRequest, DiscoveryResponse>; } class RouteDiscoveryServiceImpl implements RouteDiscoveryServiceHandlers { streamRoutes(stream: grpc.ServerDuplexStream<DiscoveryRequest, DiscoveryResponse>): void { stream.on('data', (request: DiscoveryRequest) => { console.log('Received request:', request); // Fetch route configurations based on the request const routes = this.fetchRoutes(request); // Construct the DiscoveryResponse const response: DiscoveryResponse = { versionInfo: 'v1', // Replace with your versioning logic resources: routes, typeUrl: 'envoy.config.route.v3.RouteConfiguration', // Replace with your resource type URL nonce: 'some-nonce', // Generate a unique nonce controlPlane: null, // Replace if you have control plane info }; stream.write(response); }); stream.on('end', () => { console.log('Stream ended'); stream.end(); }); stream.on('error', (err) => { console.error('Stream error:', err); stream.end(); }); } private fetchRoutes(request: DiscoveryRequest): any[] { // Implement your logic to fetch route configurations // based on the request parameters (e.g., resourceNames, versionInfo) // This is where you would access your data store. console.log("fetching routes"); return [ { name: 'route1', domains: ['example.com'] }, { name: 'route2', domains: ['test.com'] }, ]; // Replace with actual route configurations } } function main() { const server = new grpc.Server(); // server.addService(RouteDiscoveryServiceService, new RouteDiscoveryServiceImpl()); server.addService({streamRoutes: {path: "/envoy.service.discovery.v3.RouteDiscoveryService/StreamRoutes", requestStream: true, responseStream: true, requestSerialize: (arg: any) => Buffer.from(JSON.stringify(arg)), requestDeserialize: (arg: Buffer) => JSON.parse(arg.toString()), responseSerialize: (arg: any) => Buffer.from(JSON.stringify(arg)), responseDeserialize: (arg: Buffer) => JSON.parse(arg.toString())}}, new RouteDiscoveryServiceImpl()); server.bindAsync('0.0.0.0:50051', grpc.ServerCredentials.createInsecure(), (err, port) => { if (err) { console.error('Failed to bind:', err); return; } console.log(`Server listening on port ${port}`); server.start(); }); } main(); ``` **Important Considerations:** * **Error Handling:** Implement robust error handling to gracefully handle unexpected situations. * **Logging:** Add logging to help you debug and monitor your MCP server. * **Security:** Secure your gRPC server using TLS/SSL. * **Scalability:** Consider the scalability of your MCP server, especially if you're serving a large number of Envoy proxies. * **Testing:** Thoroughly test your MCP server to ensure that it's working correctly. **Next Steps:** 1. **Tell me which MCP SDK you're using.** 2. **Tell me what kind of MCP server you want to create.** 3. **Tell me your desired level of detail.** Once I have this information, I can provide you with more specific and helpful guidance.

visualcrossing

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

claude-session-continuity-mcp

Zero-config session continuity for Claude Code. Automatically captures and restores project context across sessions using Claude Hooks.

MORAGENT AI Agent Studio

Turns Claude Code into an AI Agent Studio with a guided menu and 11 tools to design, create, and manage multi-agent projects without coding.

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

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.