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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。

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87,640

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36,828

最近生成

2026-05-22

文档知识未说明

An MCP server that enables users to read, search, and analyze PDF documents. It provides tools for extracting text, viewing metadata, searching content with context, and generating word statistics.

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An MCP server for local PDF manipulation including merging, splitting, rotating, watermarking, and text extraction. It works with various MCP-compatible clients and processes PDFs entirely on-device without cloud services.

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A comprehensive MCP server that provides intelligent access to Perplexity AI's search and reasoning models with automatic model selection, conversation management, and project-aware storage. Supports real-time search, deep research, chat sessions, and async operations for complex queries.

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Enables searching and retrieving high-quality stock photos from Pexels with advanced filtering options including orientation, size, color, and localization support.

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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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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 basic MCP server adapted from the official quickstart guide that provides weather data functionality and works with OpenAI chat completions API. Demonstrates MCP server setup with configuration examples for Claude Desktop and development tools.

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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.