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qdrant-mcp-local

Aqui está uma configuração Docker Compose para iniciar facilmente o Qdrant e o MCP-server-qdrant em um ambiente local: ```yaml version: "3.9" services: qdrant: image: qdrant/qdrant:latest ports: - "6333:6333" # Porta para a API do Qdrant - "6334:6334" # Porta para gRPC volumes: - qdrant_data:/qdrant/storage # Persistência de dados (opcional) restart: unless-stopped mcp-server-qdrant: image: your-mcp-server-qdrant-image:latest # Substitua pela sua imagem do MCP-server-qdrant ports: - "8000:8000" # Porta para a API do MCP-server-qdrant (ajuste conforme necessário) environment: QDRANT_HOST: qdrant QDRANT_PORT: 6333 # Adicione outras variáveis de ambiente necessárias para o seu MCP-server-qdrant depends_on: - qdrant restart: unless-stopped volumes: qdrant_data: # Volume para persistir os dados do Qdrant (opcional) ``` **Explicação:** * **`version: "3.9"`:** Especifica a versão do Docker Compose. * **`services:`:** Define os serviços que serão executados. * **`qdrant:`:** Define o serviço Qdrant. * **`image: qdrant/qdrant:latest`:** Usa a imagem mais recente do Qdrant do Docker Hub. * **`ports:`:** Mapeia as portas do container para o host. `6333` é a porta padrão para a API do Qdrant e `6334` para gRPC. * **`volumes:`:** Monta um volume para persistir os dados do Qdrant. Isso é opcional, mas recomendado para evitar a perda de dados ao reiniciar o container. * **`restart: unless-stopped`:** Reinicia o container automaticamente, a menos que ele tenha sido parado manualmente. * **`mcp-server-qdrant:`:** Define o serviço MCP-server-qdrant. * **`image: your-mcp-server-qdrant-image:latest`:** **IMPORTANTE:** Substitua `your-mcp-server-qdrant-image:latest` pelo nome da sua imagem Docker do MCP-server-qdrant. * **`ports:`:** Mapeia a porta do container para o host. `8000` é um exemplo, ajuste conforme a porta que seu MCP-server-qdrant usa. * **`environment:`:** Define variáveis de ambiente para o container. * **`QDRANT_HOST: qdrant`:** Define o hostname do Qdrant. Como estamos usando o Docker Compose, podemos usar o nome do serviço (`qdrant`) como hostname. * **`QDRANT_PORT: 6333`:** Define a porta da API do Qdrant. * **Adicione outras variáveis de ambiente:** Seu MCP-server-qdrant pode precisar de outras variáveis de ambiente, como chaves de API, configurações de banco de dados, etc. Adicione-as aqui. * **`depends_on:`:** Garante que o Qdrant seja iniciado antes do MCP-server-qdrant. * **`restart: unless-stopped`:** Reinicia o container automaticamente, a menos que ele tenha sido parado manualmente. * **`volumes:`:** Define os volumes que serão usados. * **`qdrant_data:`:** Define um volume nomeado para persistir os dados do Qdrant. **Como usar:** 1. **Salve o código acima em um arquivo chamado `docker-compose.yml`.** 2. **Substitua `your-mcp-server-qdrant-image:latest` pelo nome da sua imagem Docker do MCP-server-qdrant.** 3. **Adicione as variáveis de ambiente necessárias para o seu MCP-server-qdrant na seção `environment`.** 4. **Abra um terminal no mesmo diretório do arquivo `docker-compose.yml`.** 5. **Execute o comando `docker-compose up -d`.** Isso irá baixar as imagens, criar os containers e iniciá-los em modo detached (em segundo plano). **Para parar os containers:** Execute o comando `docker-compose down`. **Observações:** * **Persistência de dados:** O volume `qdrant_data` é usado para persistir os dados do Qdrant. Se você não precisar persistir os dados, você pode remover a seção `volumes` do serviço `qdrant` e a definição do volume `qdrant_data`. * **Variáveis de ambiente:** Certifique-se de configurar corretamente as variáveis de ambiente para o seu MCP-server-qdrant. * **Portas:** Ajuste as portas conforme necessário. * **Imagem do MCP-server-qdrant:** Lembre-se de substituir `your-mcp-server-qdrant-image:latest` pelo nome da sua imagem Docker. * **Rede:** O Docker Compose cria uma rede padrão para os containers, permitindo que eles se comuniquem usando seus nomes de serviço como hostnames. Este é um exemplo básico. Você pode precisar ajustar a configuração para atender às suas necessidades específicas. Consulte a documentação do Qdrant e do MCP-server-qdrant para obter mais informações.

MCP REST

Expõe um servidor SSE MCP como endpoints REST para ferramentas. Apenas para testes.

claude-senator-mcp

An MCP server that enables inter-Claude communication and context sharing for seamless collaboration and conversation forking between multiple AI instances. It uses a smart pointer architecture to efficiently share project data and conversation history without interrupting ongoing tasks.

Structured-Argumentation

A group of model context protocol servers provide cognitive enhancement tools for large language models.

ForexFactory MCP Server

Enables access to ForexFactory economic calendar data through MCP resources and tools. Supports retrieving economic events by time periods for integration with trading assistants and agentic workflows.

Markdownify MCP Server

Converts various file types (PDF, images, audio, DOCX, XLSX, PPTX) and web content (YouTube videos, web pages, Bing search results) into Markdown format for easy reading and sharing.

ChronoSphere AI Date-Time MCP

Provides AI agents with precise, locale-aware date and time data across multiple timezones through a Model Context Protocol API built with TypeScript/Node.js.

TimeCard MCP

An MCP server that automates TimeCard timesheet management using Playwright browser automation. It enables users to manage projects, activities, and daily hours entries through natural language interactions.

Gemini Chat MCP

Enables chatting with Google Gemini AI models and custom Gems, supporting text and image generation with automatic cookie synchronization via Chrome extension.

App Store Connect MCP Server

Enables AI assistants to access App Store Connect data including financial metrics, subscription analytics, app performance data, and revenue insights. Provides real-time iOS app metrics through secure API integration with rate limiting and comprehensive reporting capabilities.

Stape MCP Server

Enables interaction with the Stape platform through the Model Context Protocol. Provides access to Stape's server-side tagging and data management capabilities with support for both US and EU endpoints.

OpenStreetMap MCP Server

A comprehensive MCP server providing 30 tools for geocoding, routing, and OpenStreetMap data analysis. It enables AI assistants to search for locations, calculate travel routes, and perform quality assurance checks on map data.

SSI Stock Data MCP Server

A Model Context Protocol server that enables AI assistants to query Vietnam stock intraday data from SSI FastConnect API, providing access to stock codes, detailed information, index data, and OHLC information through natural language queries.

MCP Employee API Server

Enables AI assistants to manage employee data through a REST API with full CRUD operations. Provides tools to create, read, update, and delete employee records via the Model Context Protocol.

Basecamp MCP Server

Enables AI assistants to interact with Basecamp projects through natural language commands. Supports managing projects, to-do lists, messages, and creating tasks with full content rendering capabilities.

LearnMCP Server

Extracts and summarizes learning content from YouTube videos, PDFs, and web articles to provide context for project-based learning. It features automated background processing and integrates with Forest's HTA builder for informed task generation.

UniCloudDB-MCP

A database operation tool for uniCloud based on MCP protocol that allows AI assistants to perform database CRUD operations through standardized interfaces.

Playwright MCP Server

Um servidor de Protocolo de Contexto de Modelo que permite que LLMs interajam com páginas web, capturem screenshots, gerem código de teste, extraiam dados de páginas web e executem JavaScript em um ambiente de navegador real.

Remote MCP with Azure Functions (Python)

This project enables running a custom remote MCP server on Azure Functions with Python, allowing developers to build cloud-based Model Context Protocol tools that are secured by design using keys and HTTPS.

SaaSus Docs MCP Server

Enables searching and retrieving documentation content from SaaSus Platform through MCP-compatible clients like Claude Desktop and Cursor. Provides tools to search for relevant articles, get full content from specific URLs, and access the complete sitemap of SaaSus documentation.

YOURLS-MCP

A Model Control Protocol server that enables Claude Desktop to interact with your self-hosted YOURLS URL shortener, allowing Claude to automatically shorten URLs, expand short URLs, and retrieve click statistics.

Obsidian MCP

Um servidor de Protocolo de Contexto de Modelo que permite que assistentes de IA leiam, escrevam e manipulem notas no seu cofre do Obsidian através de uma interface padronizada.

MCP Worktree Voting Server

Enables parallel implementation of tasks using git worktrees, allowing you to create multiple variants of a solution, evaluate them side-by-side, and select the best one.

MCP OpenAI Tools

Provides access to OpenAI's advanced models (including o3) with integrated web search, Python code interpreter, and combined analysis capabilities. Enables users to perform web searches, execute code in sandboxed environments, and combine search with analysis through natural language.

Simple TypeScript MCP Server

A TypeScript template for building Model Context Protocol servers that implements basic note-taking CRUD operations with JSON responses.

Looker Admin MCP

Provides over 60 administrative tools for managing users, groups, roles, schedules, alerts, and content access within Looker through the Model Context Protocol. It enables full administration of Looker environments, including permission management and system configuration.

MCP Database Tools Server

An MCP server designed to automate Django database setup and management, including PostgreSQL database creation and extension configuration. It enables users to update environment files and execute Django management commands through integrated tools like VS Code Copilot.

BusyBee

Autonomous TDD coding agent that converts specifications into feature lists and implements them using test-driven development with pause/resume capabilities, live progress monitoring, and automatic git commits.