The Documentation MCP Server gives AI assistants structured access to documentation across Markdown and OpenAPI files, with both an MCP endpoint for AI clients and a web interface for browsing.
Full-text search (search_documentation): Search with relevance scoring, optional category filtering, configurable result limits, and hierarchical breadcrumb context.
Navigate documentation (navigate_to): Navigate to a specific URI (e.g., docs://guides/security) and retrieve parent, children, and breadcrumb context.
Table of contents (get_table_of_contents): Retrieve the complete documentation hierarchy as a tree, with an optional max depth parameter.
Retrieve a document (get_document): Fetch the full content and metadata for any document by its URI.
Tag-based search (search_by_tags): Find documents by metadata tags (OR logic) with optional category filtering and result limits.
List all tags (get_all_tags): Get all unique tags across documentation, with optional category filtering and document counts per tag.
Generate PDF : Export all documentation as a PDF (requires optional PDF dependency).
Enables AI assistants to navigate and search documentation organized in markdown files, supporting hierarchical structures and YAML frontmatter for metadata extraction.
Provides support for parsing and querying documentation written in the MDX format, allowing assistants to access structured content and associated metadata.
Supports the loading and querying of OpenAPI 3.x specifications in YAML format and enables the extraction of document metadata from YAML frontmatter.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Documentation MCP Server search for the getting started guide and summarize the setup steps"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP server that gives AI assistants structured access to your documentation. Supports markdown with YAML frontmatter, OpenAPI specs, full-text search, a web interface, and PDF generation.
Install from PyPI:
pip install your-docs-mcpWith semantic search (recommended):
pip install "your-docs-mcp[vector]" --extra-index-url https://download.pytorch.org/whl/cpuWith PDF generation:
pip install "your-docs-mcp[pdf]"All features:
pip install "your-docs-mcp[vector,pdf]" --extra-index-url https://download.pytorch.org/whl/cpuPDF generation requires system packages:
macOS: brew install pandoc basictex
Ubuntu/Debian: sudo apt install pandoc texlive-xetex texlive-latex-extra
Related MCP server: Dedalus MCP Documentation Server
export DOCS_ROOT=/path/to/your/docs
your-docs-serverOpen http://localhost:8123 to browse your docs. The MCP server is also running for AI clients.
Claude Desktop - edit ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"docs": {
"command": "your-docs-mcp",
"env": {
"DOCS_ROOT": "/absolute/path/to/your/docs"
}
}
}
}VS Code - create .vscode/mcp.json:
{
"servers": {
"docs": {
"command": "your-docs-mcp",
"env": {
"DOCS_ROOT": "${workspaceFolder}/docs"
}
}
}
}Tool | Description |
| Full-text search with relevance scoring |
| Navigate to a doc by URI (e.g. |
| Get the full documentation hierarchy |
| Retrieve a document and its metadata |
| Filter docs by tags |
| List all tags across documentation |
| Generate a PDF of all documentation |
Markdown with YAML frontmatter:
---
title: Getting Started
tags: [guide, quickstart]
order: 1
---
# Getting Started
Your content here...OpenAPI 3.x (.yaml or .json) is also supported.
Key environment variables:
DOCS_ROOT=/path/to/docs # Required: documentation root directory
MCP_DOCS_CACHE_TTL=3600 # Cache TTL in seconds
MCP_DOCS_SEARCH_LIMIT=10 # Max search results
MCP_DOCS_WEB_PORT=8123 # Web server port
LOG_LEVEL=INFO # DEBUG, INFO, WARNING, ERRORyour-docs-server # MCP server + web interface
your-docs-mcp # MCP server only
your-docs-web # Web interface onlygit clone https://github.com/esola-thomas/your-docs-mcp
cd your-docs-mcp
pip install -e ".[dev,vector,pdf]" --extra-index-url https://download.pytorch.org/whl/cpu
pytest
ruff check .See the contributing guide for details on running tests, code style, and submitting pull requests. Open an issue to report bugs or request features.
MIT - see LICENSE for details.
Get a list of all unique tags defined across the documentation. Optionally filter by category and include document counts per tag.
Inputs: category, include_counts.
Get full content and metadata for a specific document by URI.
Inputs: uri (required).
Get the complete documentation hierarchy as a table of contents tree.
Inputs: max_depth.
Search documentation by metadata tags and category.
Inputs: tags (required), category, limit.
Search documentation with full-text search. Returns results with hierarchical context (breadcrumbs).
Inputs: query (required), category, limit.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Scored across 6 tools
Most tools are clearly distinct: search_documentation is full-text, search_by_tags is metadata-based, get_document retrieves content, and navigation tools handle hierarchy. There is slight overlap between search_documentation and search_by_tags, but the descriptions clarify the different search modes.
Tool names consistently use verb_noun pattern (search_documentation, navigate_to, get_table_of_contents, get_document, get_all_tags). search_by_tags follows the pattern, though 'navigate_to' is slightly less descriptive than the others but still consistent in style.
Six tools is well-scoped for a documentation server, covering search, navigation, and metadata retrieval without unnecessary redundancy. Each tool serves a distinct purpose in the documentation workflow.
The surface covers core documentation needs: full-text search, tag search, navigation, TOC, and document retrieval. Minor gaps include no obvious way to get related documents or search within a specific subtree, but the core workflows are complete.
Versioned documentation registry and semantic search for AI tools and coding assistants.
Convert PDFs, Office docs, websites and audio into AI-ready Markdown, then search and ask your docs.
Governed, auditable knowledge your team curates for its AI assistants, self-hostable
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