Discover Awesome MCP Servers

research-mcp

A citation-finding research assistant for scientists, exposed as an MCP server that extracts claims from draft paragraphs, searches multiple academic sources, scores paper quality, and explains recommendations.

Qobrix CRM MCP Server

A read-only MCP server providing 56 tools to query Qobrix real-estate CRM data, covering listings, leads, viewings, offers, contracts, analytics, and more, with RESO Data Dictionary alignment and caching support.

Cirvoy-Kiro MCP Integration

Enables seamless task synchronization between Kiro IDE and the Cirvoy project management platform. It provides tools to create, list, and update tasks directly within the IDE using the Model Context Protocol.

Seleniumboot MCP

Python MCP server for Selenium WebDriver — 84 tools for browser automation, element interactions, assertions, self-healing locators, and codegen for Java TestNG / JUnit 5 / Cucumber / pytest / C# NUnit / Playwright and CI pipelines (GitHub Actions / Jenkins / GitLab CI). No ChromeDriver setup needed.

ZoneMender

MCP server for AI agents to safely manage Cloudflare DNS, DMARC, SPF, BIMI, and Email Routing with dry-run-first and approval-based writes.

phren

A persistent memory server for AI agents that stores findings, tasks, and patterns in Markdown files within a git repository, enabling context injection across multiple AI tools.

comfyui-mcp-server-node

A lightweight MCP server that bridges AI agents with a local ComfyUI instance to generate and iteratively refine images, audio, and video through conversational tool calls.

StarUML MCP Server

Enables creating diagrams or generating code from diagrams in StarUML via prompts.

FGTS: Guia de Arrecadação

Enables consultation of FGTS (Brazilian severance guarantee fund) collection guides from official sources via a hosted, read-only MCP server. Supports natural language queries through any MCP client with pay-per-use credit.

MegaMem

Syncs Obsidian notes into a temporal knowledge graph and exposes 23 MCP tools for AI assistants to read, search, and write to your vault, enabling persistent memory across conversations.

Amazon Business Integrations MCP Server

Provides AI-enabled access to Amazon Business API documentation, sample code, and troubleshooting resources. Enables developers to search and retrieve API documentation, generate integration code, and get guided solutions for common errors during the API integration process.

사주 MCP 대시보드

Korean traditional saju (four pillars) fortune analysis MCP server with a premium web GUI dashboard for local data persistence, 100-point scoring, and history management.

blender-MCP

Enables safe, repeatable preparation of game characters in Blender through high-level MCP tools for validation, import, normalization, action renaming, and GLB export, with dry-run by default and loopback-only security.

MCP MySQL Server

Enables interaction with MySQL databases (including AWS RDS and cloud instances) through natural language. Supports database connections, query execution, schema inspection, and comprehensive database management operations.

iLoveVideoEditor MCP Server

Enables AI agents to render videos with iLoveVideoEditor by listing templates, filling in variables, submitting render jobs, polling status, and retrieving download URLs.

zoom-mcp

MCP server for Zoom's Users Admin REST API, providing 71 tools to manage users, groups, contact groups, and divisions for account creation and validation.

MCP Tool Server

A Model Context Protocol server that advertises tools with JSON schemas and executes tool calls safely, enabling AI agents to perform actions on real systems.

x64dbg MCP server

x64dbg 디버거를 위한 MCP 서버

Internship Scout & Quality of Life MCP Server

Integrates Eurostat quality-of-life metrics and real-time job searching to help users find international internships in high-ranking European cities. It enables ranking cities based on personalized criteria like safety or transport and retrieves structured internship listings via the Tavily API.

EditX

An MCP server for taking screenshots and editing images with annotations, shapes, and effects.

mcp-sphinx-docs

Converts Sphinx documentation (RST) to Markdown optimized for LLMs, enabling easy consumption of technical documentation by language models.

MCP with Langchain Sample Setup

## 샘플 MCP 서버 & 클라이언트 설정 (LangChain 호환) 다음은 LangChain과 호환되는 샘플 MCP (Message Passing Communication) 서버 및 클라이언트 설정입니다. 이 예제는 간단한 텍스트 기반 통신을 보여주며, 필요에 따라 더 복잡한 데이터 구조와 프로토콜을 지원하도록 확장할 수 있습니다. **주의:** 이 코드는 예시이며, 실제 프로덕션 환경에서는 보안, 오류 처리, 확장성 등을 고려하여 더 robust하게 구현해야 합니다. **1. MCP 서버 (Python):** ```python import socket import threading HOST = 'localhost' # 서버 IP 주소 PORT = 12345 # 서버 포트 번호 def handle_client(conn, addr): """클라이언트 연결을 처리하는 함수""" print(f"연결됨: {addr}") try: while True: data = conn.recv(1024) # 최대 1024 바이트 수신 if not data: break message = data.decode('utf-8') print(f"수신: {message}") # LangChain과 통합하는 부분 (예시) # 여기에서 LangChain 모델을 사용하여 메시지를 처리하고 응답을 생성할 수 있습니다. response = f"서버 응답: {message.upper()}" # 간단한 예시: 메시지를 대문자로 변환 conn.sendall(response.encode('utf-8')) except Exception as e: print(f"오류 발생: {e}") finally: print(f"연결 종료: {addr}") conn.close() def start_server(): """MCP 서버를 시작하는 함수""" server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) server_socket.bind((HOST, PORT)) server_socket.listen() print(f"서버 시작: {HOST}:{PORT}") while True: conn, addr = server_socket.accept() thread = threading.Thread(target=handle_client, args=(conn, addr)) thread.start() if __name__ == "__main__": start_server() ``` **설명:** * **`handle_client(conn, addr)`:** 클라이언트 연결을 처리하는 함수입니다. 클라이언트로부터 데이터를 수신하고, LangChain 모델을 사용하여 처리한 후 응답을 다시 클라이언트로 보냅니다. * **`start_server()`:** 서버 소켓을 생성하고 클라이언트 연결을 수락하는 함수입니다. 각 클라이언트 연결은 별도의 스레드에서 처리됩니다. * **LangChain 통합:** `handle_client` 함수 내에서 LangChain 모델을 사용하여 수신된 메시지를 처리하고 응답을 생성하는 부분을 구현해야 합니다. 예시에서는 간단하게 메시지를 대문자로 변환하여 응답합니다. **2. MCP 클라이언트 (Python):** ```python import socket HOST = 'localhost' # 서버 IP 주소 PORT = 12345 # 서버 포트 번호 def send_message(message): """서버에 메시지를 보내고 응답을 받는 함수""" with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as client_socket: client_socket.connect((HOST, PORT)) client_socket.sendall(message.encode('utf-8')) data = client_socket.recv(1024) return data.decode('utf-8') if __name__ == "__main__": message = "안녕하세요, 서버!" response = send_message(message) print(f"서버 응답: {response}") ``` **설명:** * **`send_message(message)`:** 서버에 메시지를 보내고 응답을 받는 함수입니다. * 클라이언트 소켓을 생성하고 서버에 연결합니다. * 메시지를 서버에 보내고 서버로부터 응답을 받습니다. * 받은 응답을 반환합니다. **LangChain과의 통합:** LangChain을 이 설정과 통합하려면 다음 단계를 따르세요. 1. **LangChain 설치:** `pip install langchain` 2. **LangChain 모델 로드:** 서버 코드에서 LangChain 모델 (예: LLMChain)을 로드합니다. 3. **메시지 처리:** `handle_client` 함수 내에서 수신된 메시지를 LangChain 모델에 입력하고 응답을 생성합니다. 4. **응답 전송:** 생성된 응답을 클라이언트로 보냅니다. **예시 (LangChain 통합):** ```python # 서버 코드 (handle_client 함수 내) from langchain.llms import OpenAI from langchain.chains import LLMChain from langchain.prompts import PromptTemplate # OpenAI API 키 설정 (환경 변수 또는 직접 설정) import os os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY" # LangChain 모델 로드 llm = OpenAI(temperature=0.9) prompt = PromptTemplate( input_variables=["message"], template="Translate the following message to Korean: {message}" ) chain = LLMChain(llm=llm, prompt=prompt) # 메시지 처리 response = chain.run(message) conn.sendall(response.encode('utf-8')) ``` **참고:** * 위 코드는 OpenAI API를 사용하며, API 키를 설정해야 합니다. * LangChain 모델의 종류와 프롬프트는 필요에 따라 변경할 수 있습니다. * 에러 처리 및 로깅을 추가하여 코드의 안정성을 높이는 것이 좋습니다. 이 샘플 코드는 LangChain과 호환되는 기본적인 MCP 서버 및 클라이언트 설정을 제공합니다. 이 코드를 기반으로 필요에 따라 기능을 확장하고 LangChain 모델을 통합하여 다양한 애플리케이션을 구축할 수 있습니다.

Spotinst MCP Server

An MCP server for the Spot.io API that enables management of AWS and Azure Ocean clusters across multiple accounts. It provides tools for cluster inventory, node management, cost analysis, and scaling operations through natural language.

web-scrapper-mcp

A portable MCP server that scrapes SPA/JS-rendered webpages and extracts structured API endpoint data. It uses headless Chromium to render JavaScript and provides tools for scraping, endpoint extraction, screenshots, and database seeding.

mcp-mysql-apifox

MCP server for executing MySQL SQL, managing Apifox API documentation, and parsing/executing curl commands.

mcp-data-analysis-agent

A local-first MCP server that provides safe, governed read-only SQL access to SQLite and PostgreSQL, with validation, limits, task tracking, and report generation.

Crow

MCP server for read-only CrowdStrike Falcon visibility, enabling AI agents to list and retrieve detections, alerts, hosts, incidents, perform IOC lookups, and run LogScale queries.

Vora

First Voice AI MCP for AI Agents

DevTools MCP

Enables developers to explain error messages, validate and format JSON, generate regex patterns from descriptions, and summarize text via an LLM, all through MCP-connected clients.

Markdown MCP Server

An MCP (Model Context Protocol) server for efficiently managing Markdown documents in Cursor AI IDE, supporting CRUD operations, search, and metadata management.