Discover our curated collection of MCP servers for learning & documentation. Browse 1780servers and find the perfect MCPs for your needs.
Extracts clean, focused documentation from websites for human readers and LLM consumption.
Provides a template repository for running a local language model with an included knowledge base.
Manages and retrieves knowledge using a natural language interface, powered by LLM analysis.
Provides an interactive tutorial to learn Model Context Protocol (MCP) development.
Connects local Obsidian vaults stored in iCloud to AI via the Model Context Protocol (MCP).
Enables AI models to interact with the Art Institute of Chicago Collection through natural language, making artworks available as a Resource.
Creates a massive, searchable knowledge base for AI assistants from curated and auto-discovered repositories.
Provides documentation and configuration for an HTTP-based server facilitating search and discovery of Model Context Protocols.
Connects AI tools and IDEs to your product documentation through a hosted Retrieval-Augmented Generation (RAG) layer.
Streamlines software development projects by providing structured workflow tools across various complexity levels and phases.
Empowers AI coding assistants with comprehensive project memory and context awareness for enhanced development workflows.
Provides intelligent querying and analysis capabilities for the Ballerina Language Server codebase.
Renders Markdown content or files into PDF documents via a Model Context Protocol (MCP) server.
Provides extensive knowledge about the Backstage framework for plugin development and customization.
Empowers AI assistants with intelligent keyword search and comprehensive access to Islamic resources, including the Quran, Hadith, Tafsir, and audio recitations.
Empowers inventors to draft examination-ready patent applications by leveraging Claude AI with instant access to USPTO rules and millions of patents.
Enables AI to manage tasks and Pomodoro sessions through natural language interaction, boosting focus and productivity.
Indexes Bevy documentation, embeds it into a vector database, and enables similarity search for queries.
Transforms diverse documentation into a searchable, private knowledge base with multi-collection isolation and local vector search.
Provides fast semantic code search for AI agents, enabling them to find symbols, references, and callers across any codebase.
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