learning & documentation를 위한 엄선된 MCP 서버 컬렉션을 찾아보세요. 1818개의 서버를 탐색하고 필요에 맞는 완벽한 MCP를 찾아보세요.
Connects GitHub code to Claude.ai by extracting code from repositories using the Pera1 service.
Enables LLMs to interact with Kibela content by providing an MCP server implementation for the Kibela API.
Provides an integrated server and UI panel within VS Code/Cursor for retrieving and displaying command-line tool documentation.
Caches a codebase as a graph to provide LLMs with efficient code memory.
Provides context-aware code assistance, explanations, and suggestions directly related to local project files.
Converts Compiled HTML Help (CHM) files to Markdown format for easier accessibility and version control.
Builds a RAG-based HR chatbot that provides workplace rules using a localhost MCP server.
Simulates the classic Lemonade Stand game, enabling play through Claude Desktop using the Model Context Protocol (MCP).
Generates comprehensive Learning Hour content and interactive whiteboard sessions for technical coaches to foster team technical excellence.
Provides real-time information access using Google Gemini's grounding capabilities for MCP-compatible clients.
Equips AI agents with instant, up-to-date access to official Apple developer documentation, design guidelines, and video content via Retrieval-Augmented Generation.
Provides AI assistants with a human-like temporal memory system, featuring natural forgetting and reinforcement based on cognitive science principles.
Ensures the accuracy of academic citations by verifying them against the CrossRef database, preventing large language models from hallucinating references.
Provides intelligent, agent-centric access to React Native Godot documentation, examples, and implementation guides for large language models.
Provides RimWorld source code search and browsing capabilities within an MCP server environment.
Convert Markdown documents into professional Word documents with extensive formatting, styling, and layout capabilities.
Enables AI agents to perform intent-based searches across project documentation, automatically mapping natural language queries to the right sources.
Powers AI agents with a self-hosted RAG engine, ingesting local web documents and PDFs to provide grounded context via the Model Context Protocol (MCP).
Enhances Obsidian or markdown vaults with AI-powered semantic knowledge graph navigation using vector embeddings and PostgreSQL+pgvector.
Empower large language models with a persistent, version-controlled knowledge base to complete and reuse thoughts across sessions and agents.
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