Discover Awesome MCP Servers

mcp-gladia

Enables LLMs to transcribe, analyze, and translate audio/video content through Gladia's API.

Tanda Workforce MCP Server

Integrates Tanda Workforce API with AI assistants to manage employee schedules, timesheets, leave requests, clock in/out operations, and workforce analytics through natural language with OAuth2 authentication.

MCP demo (DeepSeek as Client's LLM)

DeepSeek API를 사용하여 MCP 클라이언트 및 서버 데모를 실행하는 방법을 알려드리겠습니다. **전제 조건:** * **DeepSeek API 키:** DeepSeek API를 사용하려면 API 키가 필요합니다. DeepSeek 웹사이트에서 API 키를 얻을 수 있습니다. * **Python 환경:** Python 3.6 이상이 설치되어 있어야 합니다. * **필요한 라이브러리:** 다음 라이브러리를 설치해야 합니다. * `grpcio` * `protobuf` * `deepseek-client` (DeepSeek API 클라이언트 라이브러리) ```bash pip install grpcio protobuf deepseek-client ``` **1. MCP (Message Passing Communication) 프로토콜 정의 (protobuf):** 먼저, MCP 프로토콜을 정의하는 `.proto` 파일을 만들어야 합니다. 예를 들어, `mcp.proto`라는 파일을 만들고 다음과 같이 정의할 수 있습니다. ```protobuf syntax = "proto3"; package mcp; service MCPService { rpc SendMessage (Message) returns (Message) {} } message Message { string content = 1; } ``` **2. protobuf 파일 컴파일:** `protoc` 컴파일러를 사용하여 `.proto` 파일을 Python 코드로 컴파일합니다. ```bash python -m grpc_tools.protoc -I. --python_out=. --grpc_python_out=. mcp.proto ``` 이 명령은 `mcp_pb2.py`와 `mcp_pb2_grpc.py` 파일을 생성합니다. **3. 서버 구현:** `mcp_pb2_grpc.py`를 사용하여 서버를 구현합니다. ```python import grpc import mcp_pb2 import mcp_pb2_grpc from concurrent import futures class MCPServiceServicer(mcp_pb2_grpc.MCPServiceServicer): def SendMessage(self, request, context): print(f"Received message: {request.content}") response_content = f"Server received: {request.content}" return mcp_pb2.Message(content=response_content) def serve(): server = grpc.server(futures.ThreadPoolExecutor(max_workers=10)) mcp_pb2_grpc.add_MCPServiceServicer_to_server(MCPServiceServicer(), server) server.add_insecure_port('[::]:50051') server.start() server.wait_for_termination() if __name__ == '__main__': print("Starting gRPC server...") serve() ``` **4. 클라이언트 구현:** `mcp_pb2_grpc.py`를 사용하여 클라이언트를 구현합니다. DeepSeek API를 통합하는 부분은 클라이언트에서 이루어집니다. ```python import grpc import mcp_pb2 import mcp_pb2_grpc import os from deepseek_client import DeepSeekClient # DeepSeek API 키 설정 DEEPSEEK_API_KEY = os.environ.get("DEEPSEEK_API_KEY") # 환경 변수에서 API 키를 가져옵니다. def run(): with grpc.insecure_channel('localhost:50051') as channel: stub = mcp_pb2_grpc.MCPServiceStub(channel) # DeepSeek 클라이언트 초기화 client = DeepSeekClient(api_key=DEEPSEEK_API_KEY) message_content = "Hello, DeepSeek!" print(f"Client sending message: {message_content}") # DeepSeek API를 사용하여 메시지 처리 try: response = client.chat_completion( messages=[{"role": "user", "content": message_content}], model="deepseek-chat" # 적절한 모델 선택 ) deepseek_response = response.choices[0].message.content print(f"DeepSeek response: {deepseek_response}") # DeepSeek 응답을 서버에 전달 request = mcp_pb2.Message(content=deepseek_response) response = stub.SendMessage(request) print(f"Server response: {response.content}") except Exception as e: print(f"Error during DeepSeek API call: {e}") if __name__ == '__main__': run() ``` **5. 실행:** 1. 먼저 서버를 실행합니다. ```bash python server.py ``` 2. 그런 다음 클라이언트를 실행합니다. ```bash export DEEPSEEK_API_KEY="YOUR_DEEPSEEK_API_KEY" # API 키를 환경 변수로 설정 python client.py ``` `YOUR_DEEPSEEK_API_KEY`를 실제 DeepSeek API 키로 바꿔야 합니다. **설명:** * **`mcp.proto`:** MCP 프로토콜을 정의합니다. `Message`는 문자열 `content`를 포함하고, `MCPService`는 `SendMessage`라는 RPC 메서드를 정의합니다. * **`server.py`:** gRPC 서버를 구현합니다. `MCPServiceServicer`는 `SendMessage` 메서드를 구현하여 클라이언트로부터 메시지를 받고 응답합니다. * **`client.py`:** gRPC 클라이언트를 구현합니다. DeepSeek API를 사용하여 메시지를 처리하고, 서버에 메시지를 보내고 응답을 받습니다. * **DeepSeek API 통합:** 클라이언트 코드에서 `DeepSeekClient`를 사용하여 DeepSeek API를 호출합니다. `chat_completion` 메서드를 사용하여 메시지를 보내고 응답을 받습니다. 응답을 서버에 다시 보냅니다. **중요 사항:** * **API 키:** DeepSeek API 키를 안전하게 관리해야 합니다. 코드에 직접 하드코딩하지 말고 환경 변수를 사용하는 것이 좋습니다. * **모델 선택:** `deepseek-chat` 모델은 예시이며, 필요에 따라 다른 모델을 선택할 수 있습니다. DeepSeek API 문서를 참조하여 적절한 모델을 선택하십시오. * **에러 처리:** DeepSeek API 호출 중에 발생할 수 있는 예외를 처리해야 합니다. * **보안:** 이 예제는 보안되지 않은 채널을 사용합니다. 프로덕션 환경에서는 TLS를 사용하여 보안 채널을 구성해야 합니다. * **환경 변수:** `DEEPSEEK_API_KEY` 환경 변수를 설정하는 것을 잊지 마십시오. 이 예제는 기본적인 MCP 클라이언트 및 서버 데모를 DeepSeek API와 통합하는 방법을 보여줍니다. 필요에 따라 코드를 수정하고 확장할 수 있습니다. DeepSeek API 문서를 참조하여 API의 다양한 기능과 옵션을 활용하십시오.

mcp-local-redes

Local MCP server that exposes fleet telemetry queries as tools for language models, enabling natural language questions about vehicle positions, trips, and alerts. Runs on the operator's machine and returns aggregated results from a SQLite database.

MCP Server - Placeholder Implementation

An MCP server implementation in Python with placeholder tools, deployable to Azure Web App via GitHub Actions. Supports STDIO, HTTP REST, and WebSocket interfaces.

mcp-ip-api

Provides IP geolocation lookups via ip-api.com, including single and batch queries up to 100 IPs.

codex-in-claude

Call OpenAI Codex from Claude Code for independent second opinions, structured code review, and delegated coding tasks through a FastMCP plugin that drives the codex CLI safely.

figma-bridge

基于MCP协议的工具，用于从Figma获取设计节点数据并转换为结构化DSL，辅助生成Vue 3 / React / HTML组件代码。

@pipeworx/congressional-documents

Full-text search and retrieval over official congressional documents (hearings, committee reports, Congressional Record) with citations and govinfo.gov links, designed for grounding AI answers in the official record.

Freshdesk MCP Server

Enables interaction with Freshdesk API v2 to manage support tickets, contacts, agents, companies, and conversations with built-in authentication, rate limiting, and error handling.

blender-mcp-2.79

Enables Claude AI to control Blender 2.79 for 3D modeling, scene creation, and manipulation through the Model Context Protocol, with legacy compatibility for older Blender versions.

MCP Router

Automatically selects the optimal LLM model for each task in Cursor IDE by analyzing query complexity, task type, and applying customizable routing strategies across 17 different AI models.

Terminal SSH MCP Server

MCP server that enables SSH access and terminal commands for Railway containers and local shell execution.

Assert

Run real Playwright E2E tests from your AI coding agent.

MCP-123

A lightweight implementation that provides the simplest way to set up an MCP server and client, requiring just two lines of code to create a fully functional system.

metabase-mcp

Connects AI assistants to Metabase for database queries, SQL execution, dashboard management, and analytics workflows via natural language.

remote-mcp-server-authless

This is a stateless remote MCP server deployed on Cloudflare Workers without authentication, allowing users to register custom tools and connect to MCP clients such as Cloudflare AI Playground or Claude Desktop.

Sourcify Context Server

A simple notes MCP server that enables creating, listing, and summarizing text notes via resources, tools, and prompts.

ltchiptool-mcp

Enables chip-level firmware extraction and analysis for BK7231 family IoT devices via UART, supporting flash dumping, decryption, and partition extraction.

idnow-mcp-server

An MCP server that lets LLM agents operate the IDnow Trust Platform sandbox to list verification flows, create sessions, check sessions, and get session details.

Oracle HCM Cloud MCP Server by CData

Oracle HCM Cloud MCP Server by CData

ministic-fishstick

Minimal high-performance MCP server for semantic code indexing and vector search using Bun, SQLite, and Tree-Sitter. It enables AI agents to index, search, and manage codebases via tools like code_index_search and code_index_start.

Weather MCP Server

Enables asking natural language weather questions; an LLM agent selects the appropriate live weather API tool and returns an HTML-formatted answer.

PubMed MCP Server

Provides structured PubMed literature data for LLM agents, supporting search, caching, and open-access full-text downloads via the MCP protocol.

newrelic-mcp-server

Provides New Relic observability tools for AI assistants, enabling discovery, data access, alerting, incident response, and performance analytics via natural language queries.

trafikkmeldinger-mcp

Enables querying Norwegian traffic messages (roadworks, closures, detours) from Statens vegvesen, filtered by route and time window. Supports both current and future travel times, with tools for route-based and general searches.

MCPizza

An MCP server that allows AI assistants to order Domino's Pizza through an unofficial API, with features for store location, menu browsing, and order management.

Local FAISS MCP Server

Provides local vector database functionality using FAISS for document ingestion, semantic search, and Retrieval-Augmented Generation (RAG) applications with persistent storage and customizable embedding models.

Multi-Capability Proxy Server

A Flask-based server that hosts multiple tools, each exposing functionalities by calling external REST APIs through a unified interface.

spar-agent

Enables developers to compare their code predictions against AI-generated implementations, log misconceptions, and receive spaced-repetition learning feedback to guide their understanding of gaps.