"General Documentation Search" matching MCP servers:

Enables Claude to store and retrieve information with semantic search using Qdrant vector database, providing persistent memory for conversations, code, and documentation.

Local MCP server that manages a job search using Markdown files, enabling skills tracking, gap analysis, and offer pipeline management via Claude.

Enables users to access, search, and get recommendations from AWS documentation through natural language queries. Supports both global AWS documentation and AWS China documentation with tools to fetch pages, search content, and discover related resources.

Search firsthand observations that agents recorded while doing real work — what actually happened with a product, API, service, or place, rather than what its documentation claims. Agents can also write back what they observed.

Enables keyword search, filtering, and retrieval of Claude Code memory files, reducing context overhead by fetching only relevant memories on demand.

Enables AI assistants to search, retrieve, and browse Markdown documentation in a local folder, then answer questions with cited sources.

Enables semantic search over markdown files to find related notes by meaning rather than keywords, and automatically detect duplicate content before creating new notes.

Connects Claude to a Markdrop knowledge base, allowing it to search and retrieve markdown documentation for context-aware development assistance.

A Python MCP server that gives LLMs persistent, searchable access to project context — documentation, architecture decisions, and session notes.

A general-purpose MCP server providing web search, persistent memory storage, and secure code execution capabilities. It enables AI agents to search the web, store and retrieve data, and run Python/JavaScript code in sandboxed environments.

MCP server for Yandex Wiki with full-text search. Read and write pages, comments, attachments, and dynamic tables (grids); optional server-side read-only mode for agents. Docker-ready.

A Model Context Protocol server that enables AI models to perform real-time internet and knowledge searches through Higress, enhancing model responses with up-to-date information from Google, Bing, Arxiv, and internal knowledge bases.

Enables users to search and access DOOR support documentation and knowledge base articles through natural language queries. Provides fast search capabilities across support.door.com content including articles and PDFs with pre-built search indexing.

A Model Context Protocol implementation that enables AI assistants to interact with markdown documentation files, providing capabilities for document management, metadata handling, search, and documentation health analysis.

Enables access to Yuque (语雀) documentation through cookie-based authentication, supporting document retrieval, search, TOC navigation, and content management from knowledge bases via URL or namespace.

Universal documentation knowledge-graph MCP server with hybrid full-text + vector search. Indexes local files and remote sources from Notion, Jira, Obsidian, Linear, GitHub, and Confluence into a single SQLite knowledge graph, exposing it to AI agents via the Model Context Protocol.

MCP bridge for PDF Content Search — full-text PDF search with Apple Vision OCR across thousands of documents in under a second from Claude, Cursor, or any MCP client. Advanced filters (date, category, sender, amount), wildcards, boolean operators. Bridge open-source (MIT), PDF Content Search app is commercial with free iOS+Android companion scanner apps.

Provides a structured documentation system for context preservation in AI assistant environments, helping users create and manage memory banks for their projects.

An MCP server for indexing and searching Commodore 64 documentation using full-text, semantic, and fuzzy search across multiple file formats. It enables RAG-based question answering, entity extraction, and interactive timeline or knowledge graph visualizations.

Enables AI agents to access an organization's software component catalog, including entities, relationships, interfaces, documentation, and team ownership, for grounded context and impact analysis.

Enables an AI assistant to build and revise an explicit shared world model, then reason, create, narrate, remember, and give writing feedback through inspectable processes, concepts, and cuts. Supports general-purpose modeling, storytelling, agent/user memory, and ideation over one engine and graph while keeping estimates, interpretations, and observations distinct.

Turn repository documentation into deterministic, executable handoffs for coding agents through MCP, CLI, and CI—without an LLM or API key.

Enables AI agents to search through Memex conversation history and local project files to retrieve specific commands, code snippets, and technology overviews. It utilizes smart context management and faceted filtering to provide relevant search results without causing context overload.

Provides AI assistants with direct access to local Markdown documentation libraries, enabling them to list, read, and search through docs on demand.

Persistent memory system for AI agents that records episodic memories with care-weighting and emotional valence, and provides full-text search with temporal chaining and automatic consolidation.

Gives AI agents access to a persistent document workspace, letting them search, read, and update notes, guides, and project documentation across a full catalog of tools, prompts, and resources. It connects either to the hosted Cloud endpoint over Streamable HTTP with OAuth or API-key auth, or to a self-hosted instance through a stdio bridge.

Provides AI coding assistants with persistent, context-rich memory of a codebase, including documentation and git history, enabling recall across sessions.

Enables Claude to search and analyze Obsidian vault content using ripgrep with support for wiki links, backlinks, frontmatter properties, orphaned notes, and intelligent context detection showing which heading or property contains each match.

Enables fully local semantic search over a Roam Research graph via MCP, providing tools for hybrid retrieval, index refresh, and stats while keeping all content on-machine.

MCP server that gives AI coding agents persistent, semantic memory via Qdrant vector search, enabling workspace-aware codebase, documentation, and decision search.