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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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Package all your MCP servers into a single Remote MCP Gateway for one click installations, MCP analytics, creating/sharing MCP packages, and more...

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一个基于Gmail和LiveKit的个人知识助手,可通过语音与通讯互动,支持提问、摘要和笔记功能,文章内容存储在SQLite和ChromaDB中用于语义搜索。

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Enables users to manage projects and retrieve random wallpapers through a TypeScript-based MCP server. Supports project queries, creation, and Bing wallpaper retrieval with Spring Boot-style architecture.

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An MCP server for the comprehensive analysis of Swagger 2.0 and OpenAPI 3.x contracts. It allows users to extract detailed information about endpoints, request/response schemas, parameters, and security configurations from API documentation.

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A complete MCP server for Retrieval-Augmented Generation with file management and vector memory for agents. Supports multiple document formats (PDF, DOCX, TXT, MD, CSV, JSON) with semantic search using Hugging Face embeddings and ChromaDB for efficient vector storage.

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An agentic AI system that orchestrates multiple specialized AI tools to perform business analytics and knowledge retrieval, allowing users to analyze data and access business information through natural language queries.

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This MCP server enables intelligent API testing automation by combining RAG knowledge retrieval with tool execution capabilities. It allows QA engineers to perform natural language-driven API testing with contextual knowledge support.

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全面解析MCP Rag GoMCP Server的核心功能、安装配置和实用案例。作为顶级Model Context Protocol服务器,MCP Rag Go能让AI助手访问实时数据、执行操作,为您提供更智能的工作体验和自动化解决方案。

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A local RAG server that enables document indexing and sentence window retrieval across multiple file formats like PDF, MD, and DOCX. It supports both local Hugging Face models and OpenAI embeddings for efficient context-aware querying through the Model Context Protocol.

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全面解析McpragMCP Server的核心功能、安装配置和实用案例。作为顶级Model Context Protocol服务器,Mcprag能让AI助手访问实时数据、执行操作,为您提供更智能的工作体验和自动化解决方案。

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Enables AI assistants to search and retrieve information from your knowledge base using RAG (Retrieval-Augmented Generation) with hybrid search, document indexing, and ChromaDB vector storage.

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Implements Retrieval-Augmented Generation (RAG) using GroundX and OpenAI, allowing users to ingest documents and perform semantic searches with advanced context handling through Modern Context Processing (MCP).

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Provides local Retrieval-Augmented Generation (RAG) capabilities using Ollama for embeddings and ChromaDB for vector storage. It enables users to ingest and perform semantic searches across PDF, Markdown, and TXT documents within MCP-compatible clients.

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Enables direct integration with Zentao bug tracking systems through Cursor. Supports authentication, bug retrieval, searching, and listing operations for comprehensive bug management through natural language.