Discover Awesome MCP Servers

College Basketball Stats MCP Server

An MCP server for accessing college basketball statistics through the SportsData.io CBB v3 Stats API, enabling AI agents to retrieve and analyze college basketball data through natural language interactions.

pyNastran MCP Server

An MCP server that enables AI agents to interact with Nastran FEA models by reading, writing, and analyzing BDF and OP2 files. It provides tools for mesh quality assessment, geometric analysis, and automated report generation for structural engineering workflows.

Slack MCP Server

A FastMCP-based server that provides complete Slack integration for Cursor IDE, allowing users to interact with Slack API features using natural language.

DeepChat 好用的图像 MCP Server 集合

DeepChat을 위한 이미지 MCP 서버

Baby-SkyNet

Provides Claude AI with persistent, searchable memory management across sessions using SQL database, semantic analysis with multi-provider LLM support (Anthropic/Ollama), vector search via ChromaDB, and graph-based knowledge relationships through Neo4j integration.

Ghost MCP Server

Manage your Ghost blog content directly from Claude, Cursor, or any MCP-compatible client, allowing you to create, edit, search, and delete posts with support for tag management and analytics.

MolTrust

MolTrust MCP Server provides AI agents with identity verification, reputation scoring, and verifiable credentials through W3C DID-based trust infrastructure. Includes ERC-8004 on-chain agent registration and Base blockchain anchoring for tamper-proof credential verification.

Usher MCP

Enables users to search and view detailed movie information from TMDB, including cast, ratings, and showtimes, through an interactive widget interface.

Nano Banana

Generate, edit, and restore images using natural language prompts through the Gemini 2.5 Flash image model. Supports creating app icons, seamless patterns, visual stories, and technical diagrams with smart file management.

NHN Server MCP

Enables secure SSH access to servers through a gateway with Kerberos authentication, allowing execution of whitelisted commands with pattern-based security controls and server information retrieval.

amap-weather-server

MCP를 사용하는 amap-weather 서버

mcp_server

돌핀 MCP 클라이언트를 사용하여 샘플 MCP 서버를 구현하는 방법에 대한 안내입니다. **개요:** 이 가이드는 돌핀 MCP 클라이언트를 사용하여 간단한 MCP 서버를 구축하는 단계를 안내합니다. MCP (Minecraft Protocol)는 마인크래프트 클라이언트와 서버 간의 통신 프로토콜입니다. 이 샘플 서버는 기본적인 연결 수락, 핸드셰이크 처리, 그리고 간단한 상태 응답을 제공합니다. **필요 사항:** * **Python 3.6 이상:** 돌핀 MCP 클라이언트는 Python으로 작성되었습니다. * **돌핀 MCP 클라이언트 라이브러리:** `pip install dolphin-mcp`를 사용하여 설치합니다. * **네트워크 프로그래밍에 대한 기본적인 이해:** 소켓, 바이트 스트림 등에 대한 이해가 필요합니다. **구현 단계:** 1. **필요한 라이브러리 가져오기:** ```python import socket import struct import json from dolphin_mcp import packets from dolphin_mcp import utils ``` 2. **서버 설정:** ```python HOST = '127.0.0.1' # 로컬 호스트 PORT = 25565 # 마인크래프트 기본 포트 ``` 3. **소켓 생성 및 바인딩:** ```python server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) server_socket.bind((HOST, PORT)) server_socket.listen(1) # 한 번에 하나의 연결만 허용 print(f"서버가 {HOST}:{PORT}에서 시작되었습니다.") ``` 4. **클라이언트 연결 수락:** ```python client_socket, client_address = server_socket.accept() print(f"{client_address}에서 연결이 수락되었습니다.") ``` 5. **핸드셰이크 처리:** ```python # 패킷 길이 읽기 packet_length_bytes = client_socket.recv(1) packet_length = struct.unpack('>B', packet_length_bytes)[0] # 패킷 데이터 읽기 packet_data = client_socket.recv(packet_length) # 핸드셰이크 패킷 파싱 handshake_packet = packets.HandshakePacket.from_bytes(packet_data) print(f"프로토콜 버전: {handshake_packet.protocol_version}") print(f"서버 주소: {handshake_packet.server_address}") print(f"서버 포트: {handshake_packet.server_port}") print(f"다음 상태: {handshake_packet.next_state}") next_state = handshake_packet.next_state ``` 6. **상태 요청 처리 (상태 1):** ```python if next_state == 1: # 상태 요청 패킷 받기 (길이 1바이트, 내용 0x00) status_request_length_bytes = client_socket.recv(1) status_request_length = struct.unpack('>B', status_request_length_bytes)[0] status_request_data = client_socket.recv(status_request_length) # 상태 응답 생성 status_response = { "version": { "name": "샘플 서버", "protocol": handshake_packet.protocol_version }, "players": { "max": 100, "online": 0, "sample": [] }, "description": { "text": "돌핀 MCP 샘플 서버" } } status_response_json = json.dumps(status_response) status_response_bytes = status_response_json.encode('utf-8') status_response_length = len(status_response_bytes) # 패킷 길이와 데이터 전송 response_packet = struct.pack('>B', status_response_length) + status_response_bytes client_socket.sendall(response_packet) # Ping 요청 처리 (선택 사항) ping_request_length_bytes = client_socket.recv(1) ping_request_length = struct.unpack('>B', ping_request_length_bytes)[0] ping_request_data = client_socket.recv(ping_request_length) ping_payload = struct.unpack('>q', ping_request_data)[0] # long 타입으로 언팩 # Ping 응답 생성 ping_response = struct.pack('>q', ping_payload) ping_response_length = len(ping_response) # 패킷 길이와 데이터 전송 ping_response_packet = struct.pack('>B', ping_response_length) + ping_response client_socket.sendall(ping_response_packet) ``` 7. **로그인 처리 (상태 2 - 구현하지 않음):** ```python elif next_state == 2: print("로그인 요청은 아직 구현되지 않았습니다.") # TODO: 로그인 처리 구현 ``` 8. **연결 종료:** ```python client_socket.close() server_socket.close() print("연결이 종료되었습니다.") ``` **전체 코드 예시:** ```python import socket import struct import json from dolphin_mcp import packets from dolphin_mcp import utils HOST = '127.0.0.1' PORT = 25565 server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) server_socket.bind((HOST, PORT)) server_socket.listen(1) print(f"서버가 {HOST}:{PORT}에서 시작되었습니다.") client_socket, client_address = server_socket.accept() print(f"{client_address}에서 연결이 수락되었습니다.") # 핸드셰이크 packet_length_bytes = client_socket.recv(1) packet_length = struct.unpack('>B', packet_length_bytes)[0] packet_data = client_socket.recv(packet_length) handshake_packet = packets.HandshakePacket.from_bytes(packet_data) print(f"프로토콜 버전: {handshake_packet.protocol_version}") print(f"서버 주소: {handshake_packet.server_address}") print(f"서버 포트: {handshake_packet.server_port}") print(f"다음 상태: {handshake_packet.next_state}") next_state = handshake_packet.next_state if next_state == 1: # 상태 요청 status_request_length_bytes = client_socket.recv(1) status_request_length = struct.unpack('>B', status_request_length_bytes)[0] status_request_data = client_socket.recv(status_request_length) status_response = { "version": { "name": "샘플 서버", "protocol": handshake_packet.protocol_version }, "players": { "max": 100, "online": 0, "sample": [] }, "description": { "text": "돌핀 MCP 샘플 서버" } } status_response_json = json.dumps(status_response) status_response_bytes = status_response_json.encode('utf-8') status_response_length = len(status_response_bytes) response_packet = struct.pack('>B', status_response_length) + status_response_bytes client_socket.sendall(response_packet) # Ping 요청 ping_request_length_bytes = client_socket.recv(1) ping_request_length = struct.unpack('>B', ping_request_length_bytes)[0] ping_request_data = client_socket.recv(ping_request_length) ping_payload = struct.unpack('>q', ping_request_data)[0] ping_response = struct.pack('>q', ping_payload) ping_response_length = len(ping_response) ping_response_packet = struct.pack('>B', ping_response_length) + ping_response client_socket.sendall(ping_response_packet) elif next_state == 2: print("로그인 요청은 아직 구현되지 않았습니다.") client_socket.close() server_socket.close() print("연결이 종료되었습니다.") ``` **실행 방법:** 1. 위 코드를 `mcp_server.py`와 같은 파일로 저장합니다. 2. 터미널에서 `python mcp_server.py`를 실행합니다. 3. 마인크래프트 클라이언트를 실행하고, 서버 주소를 `127.0.0.1:25565`로 설정하여 연결합니다. **주의 사항:** * 이 코드는 매우 기본적인 샘플 서버이며, 실제 마인크래프트 서버의 모든 기능을 제공하지 않습니다. * 에러 처리, 보안, 성능 최적화 등은 추가적으로 구현해야 합니다. * 돌핀 MCP 클라이언트 라이브러리의 최신 문서를 참고하여 더 많은 기능을 활용할 수 있습니다. * `struct.pack`과 `struct.unpack`을 사용하여 바이트를 패킹/언패킹할 때, 바이트 순서(endianness)와 데이터 타입을 정확하게 지정해야 합니다. 위 예시에서는 `>` (big-endian)과 `B` (unsigned byte), `q` (long long)를 사용했습니다. **다음 단계:** * 로그인 처리 구현 * 채팅 기능 구현 * 플레이어 위치 정보 처리 * 청크 데이터 전송 * 더 많은 마인크래프트 프로토콜 패킷 처리 이 가이드가 돌핀 MCP 클라이언트를 사용하여 마인크래프트 서버를 구축하는 데 도움이 되기를 바랍니다. 궁금한 점이 있으면 언제든지 질문해주세요.

mcp-gsheets

mcp-gsheets

Remote MCP Server

A server that implements the Model Context Protocol (MCP) on Cloudflare Workers, allowing AI models to access custom tools without authentication.

Transmission MCP Server

Provides tools for interacting with the Transmission BitTorrent client via natural language, enabling users to manage torrents, configure download settings, and monitor download activity.

MCP Redirect Server

A Model Context Protocol server built with NestJS that provides OAuth 2.1 authentication with GitHub and exposes MCP tools through Server-Sent Events transport. Enables secure, real-time communication with JWT-based protection and dependency injection.

Google Calendar MCP Server

Enables natural language queries to Google Calendar API for checking appointments, availability, and events. Supports flexible time ranges, timezone handling, and both service account and OAuth authentication methods.

Python MCP Sandbox

An interactive Python code execution environment that allows users and LLMs to safely execute Python code and install packages in isolated Docker containers.

Android Tester MCP

An MCP server for automating virtual or physical Android devices using the Gbox SDK. It enables users to manage app installations, capture screenshots, and perform complex UI actions through natural language instructions.

Kintone Book Management MCP Tool

A Model Context Protocol (MCP) server that provides a tool for retrieving and managing book information from a Kintone database application.

MusicMCP.AI

Enables AI-powered music generation through natural language commands, supporting both inspiration mode (AI-generated lyrics and style) and custom mode (user-provided lyrics and parameters) to create songs with direct download links.

Weather-server MCP Server

A TypeScript-based MCP server that provides weather information through resources and tools, allowing users to access current weather data and forecast predictions for different cities.

MCP OpenAPI Connector

Enables Claude Desktop and other MCP clients to interact with any OAuth2-authenticated OpenAPI-based API through automatic tool generation from OpenAPI specifications, with built-in token management and authentication handling.

TaskFlow MCP

A task management server that helps AI assistants break down user requests into manageable tasks and track their completion with user approval steps.

mcp-gopls

A Model Context Protocol (MCP) server that allows AI assistants like Claude to interact with Go's Language Server Protocol (LSP) and benefit from advanced Go code analysis features.

Deep Research MCP

A Model Context Protocol compliant server that facilitates comprehensive web research by utilizing Tavily's Search and Crawl APIs to gather and structure data for high-quality markdown document creation.

Jina AI Remote MCP Server

Provides web content extraction, search capabilities (web, arXiv, SSRN, images), semantic deduplication, and reranking through Jina AI's Reader, Embeddings, and Reranker APIs.

ExecuteAutomation Database Server

A Model Context Protocol server that enables LLMs like Claude to interact with SQLite and SQL Server databases, allowing for schema inspection and SQL query execution.

Cline Code Nexus

Cline이 MCP 서버 기능을 검증하기 위해 만든 테스트 저장소입니다.