learning & documentation를 위한 엄선된 MCP 서버 컬렉션을 찾아보세요. 1570개의 서버를 탐색하고 필요에 맞는 완벽한 MCP를 찾아보세요.
Demonstrates a vulnerable server with multiple clients to showcase security weaknesses for educational purposes.
Demonstrates integrating FastAPI and FastMCP to serve LLM-callable tools via the MCP protocol.
Applies professional letterhead templates to PDF and Markdown documents, offering a macOS utility and an AI-integrable MCP server.
Guides humans and AI agents through rigorous First Principles reasoning, transforming vague problems into structured hypotheses and decisions.
Integrates over 779 API documentation sets from DevDocs.io, allowing users to search and browse a vast array of developer resources directly within Claude Desktop or any MCP-compatible client.
Empowers AI agents with searchable access to any markdown content through discoverable resources and an embedded full-text search engine.
Manages a simple notes system through a Model Context Protocol server, providing resources, tools, and prompts for note interaction.
Provides an intelligent data pipeline for high-fidelity crawling and extraction of technical documentation, optimized for AI agents.
Manages and analyzes academic literature by supporting PDF import, advanced search, knowledge graph construction, and automated review generation.
Enables AI assistants to save and semantically search bookmarks using OpenAI's Retrieval Augmented Generation (RAG) capabilities.
Demonstrates a simple MCP server built with FastMCP, emphasizing modern Python environment and package management.
Integrates GitLab merge request analysis with Confluence documentation via an MCP server.
Automates the documentation of GitHub projects as blog posts by serving a Model Context Protocol (MCP) endpoint.
Provides comprehensive and up-to-date MediaWiki markup syntax documentation by dynamically fetching and consolidating information from official MediaWiki help pages for consumption by large language models.
Transforms Confluence documentation into an AI-powered knowledge base, enabling natural language queries via Gemini CLI.
Fetches README files and searches for npm packages.
Provides a foundational example for integrating custom tools with the Gemini CLI using the Model Context Protocol (MCP).
Presents learning notes and conversations from software development sessions with Claude.
Connects AI assistants and Large Language Models to GitHub, Confluence, and Databricks environments via the Model Context Protocol.
Offers a robust set of functionalities for retrieving date and time, accessing user profiles, and generating personalized greetings.
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