learning & documentation向けの厳選されたMCPサーバーコレクションをご覧ください。2378個のサーバーを閲覧し、ニーズに最適なMCPを見つけましょう。
Exposes your Zotero library to AI assistants via the Model Context Protocol.
Enables AI assistants to interact with Outline documentation services through the Model Context Protocol (MCP).
Enables semantic search and retrieval of documentation using a vector database, allowing users to add and query documentation from URLs or local files using natural language.
Provides efficient access to Svelte documentation with advanced caching and search capabilities.
Enables AI assistants to deeply understand and analyze Unreal Engine source code.
Enables LLMs to accurately interpret mathematical expressions in scientific papers by fetching and processing LaTeX source from arXiv.
Implements the Zettelkasten knowledge management methodology, enabling creation, linking, exploration, and synthesis of atomic notes.
Connects Readwise to Large Language Models (LLMs) through a local Model Context Protocol (MCP) server.
Enables AI agents to manage documents, collections, and other entities programmatically through the Outline knowledge base platform.
Enables AI agents to explore Rust crate documentation, analyze source code, and confidently build Rust projects.
Provides an API for programmatic interaction with the Dash macOS documentation browser.
Offers Svelte 5 and SvelteKit documentation as an MCP endpoint and LLM presets for AI assistants.
Provides Claude with persistent memory and workspace file access across all chats to eliminate repetitive explanations.
Provides real-time framework documentation access for Claude Code with intelligent caching, multi-source integration, and context-aware assistance.
Provides a hands-on training program for security practitioners to build AI-powered tools for threat detection, incident response, and security automation.
Provides AI full read-write access to a Logseq knowledge graph, enabling comprehensive traversal, analysis, and content management.
Provides coding agents with a persistent knowledge graph for project understanding and decision-making across development sessions.
Scores text based on rule-based patterns to identify formulaic AI writing and provide actionable advice.
Enables AI agents to navigate and retrieve documentation by structured sections, drastically improving token efficiency and context hygiene over brute-force file scanning.
Enables creating software architecture diagrams from a single model using a "models as code" approach for the C4 model.
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