"Outline" matching MCP servers:

Enables MCP clients such as Claude Desktop, Claude Code, and Cursor to search, read, create, update, organize, and archive documents in an Outline wiki, including collections and document trees. It runs locally over stdio with API-token authentication and can optionally allow reversible deletions.

Tree-structured knowledge base as an MCP server, giving LLM sessions a persistent, editable knowledge tree with browsing, annotation, and context injection.

MCP server that provides role-based access control for Outline workspaces, allowing users to interact with docs, users, and admin tools according to their assigned role.

Enables querying, searching, and managing documents in an Outline instance via the Model Context Protocol.

Enables AI coding agents to efficiently explore codebases by providing structural outlines, module digests, symbol bodies, and AST-aware grep via MCP.

Enables token-efficient web page fetching by converting HTML to Markdown with tiered access (outline, section, search) to minimize LLM context usage.

Enables a study assistant to explain topics, build study plans, and generate revision checklists, with read-only access to course outline and status resources.

Amenbo is a Japanese-web-native MCP server for low-impact, token-efficient web collection, enabling agents to fetch, outline, and screenshot web pages while respecting robots.txt and optimizing for Japanese sites.

Turns plain text or Markdown, web pages, and local PDFs into editable Markdown outlines that can be rendered as standalone, offline-capable interactive HTML mind maps. Works with any MCP client with no API key required, since the connected model writes the final outline from a draft and cleaned source text.

Enables AI agents to efficiently process large local and online PDFs through selective extraction of text, images, and metadata. It provides tools for content search and document outline navigation to optimize context window usage.

High-performance code understanding toolkit that enables batch reading of multiple files with dependency context, structural outline extraction with Java annotation awareness, and precise location of classes/methods across large codebases.

Enables AI agents to navigate JS/TS/HTML/CSS code through TypeScript, HTML, and CSS language servers, answering hover, definition, references, symbol search, outline, and diagnostics queries. Adds a cross-file index of CSS custom properties and #id/.class selectors that single-file language servers cannot provide.

Enables LLM agents to navigate source code structurally via tree-sitter over MCP, offering outline-based reading, AST pattern search, workspace-scale symbol indexing, call graph analysis, and built-in security audit queries.

MCP server that generates ISO/IEC/IEEE 29119-3 compliant test plan drafts from project information, providing a structured resource for the standard's test plan outline and a tool for draft creation.

Enables users to create and refine a research outline, queue report generation in the background, and retrieve progress or completed Markdown reports, with automatic checkpointing so unfinished work can resume after a restart.

Enables deep, recursive multi-source scientific literature research across 8+ academic databases, producing cited, evidence-graded reports with red-team review and cost details through an outline-first, user-confirmed workflow.

MCP bridge for Inkra — drive a native macOS Markdown editor (SwiftUI + AppKit, not Electron) with live KaTeX/Mermaid preview from Claude, Cursor, or any MCP client. 18 tools across document editing, outline navigation, folder browsing, bookmarks, themes, and focus/wide view modes. Bridge open-source (MIT), Inkra app available on Mac App Store and direct download.

A local MCP server that lets LLM agents read EPUB books and PDF documents with outline-first navigation, precise node reads, and local SQLite persistence.

Enables AI agents to access VS Code's LSP-based code navigation and native debugging tools (definitions, references, outline, breakpoints, stepping, stack frames, variables, and evaluate) via the MCP protocol.

Lets AI agents know things about files without reading them, supporting outline, search, slicing, SQL, log clustering, diffs, validation, and extraction for formats like PDF, DOCX, PPTX, XLSX, CSV, logs, and code.