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MCP 工具与服务目录

找到适合你的 MCP Server,快速完成接入

按功能、传输方式和来源整理 MCP Server,提供安装命令、配置方式、仓库与文档入口,方便你快速比较并接入合适的服务。

正式条目

87,640

可复制安装

36,828

最近生成

2026-05-22

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MCP server for German public procurement data (OCDS). Semantic search, tender matching, and company profiles — all from your local LLM

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Enables the generation of branded SVL equipment quote documents in .docx format using a built-in library of HVAC equipment templates. Users can search for specific models and manufacturers to create formatted quotes through natural language commands.

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An intelligent memory MCP server that provides AI applications with semantic search, entity extraction, and knowledge graph capabilities using local Redis caching and optional cloud sync. It enables LLMs to store and retrieve long-term context across sessions with high-performance multi-tier storage.

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A simple MCP server that implements a note storage system with RAG capabilities, allowing users to store notes and generate summaries of stored content.

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A lightweight server that provides persistent memory and context management for AI assistants using local vector storage and database, enabling efficient storage and retrieval of contextual information through semantic search and indexed retrieval.

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MCP対応のRAGシステム。Markdownドキュメントをベクトル化し、自然言語で高速検索。LibSQL、Qdrant、PostgreSQLに対応してます。

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Integrates RAGFlow's knowledge base API with Claude Desktop for document retrieval, semantic search across multiple datasets, and intelligent query refinement using DSPy.

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Provides a comprehensive Model Context Protocol interface for RAGFlow, enabling AI models to perform semantic retrieval, manage datasets, and handle document chunks. It supports advanced features like GraphRAG and RAPTOR for sophisticated knowledge base management and natural language querying.

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A fixed MCP server that interacts with RAGFlow for dataset management and chat operations, handling legacy endpoint errors and providing a fallback to OpenAI-compatible endpoints.

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An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.

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MapRag is a discovery + routing layer for retrieval. It indexes RAG-capable MCP servers, enriches them with structured metadata, and helps agents (and humans) quickly find the right retrieval server for a task under constraints like citations, freshness, privacy, domain, and latency. MapRag does not do RAG itself. It helps you choose the best RAG tool/server to do the retrieval.

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Enables semantic search across text documents using vector embeddings stored in PostgreSQL. Provides multiple search modalities including semantic similarity, question/answer, and style-based search through a retrieval-augmented generation system.

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A server that integrates Retrieval-Augmented Generation (RAG) with the Model Control Protocol (MCP) to provide web search capabilities and document analysis for AI assistants.

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A Model Context Protocol server that exposes Retrieval-Augmented Generation capabilities and a weather tool, allowing clients to interact with document knowledge bases and retrieve weather information.

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An API that enables document querying through a Retrieval-Augmented Generation system implemented with Memory-Controller-Policy architecture for improved maintainability and scalability.