MkDocs Search▌

by serverless-dna
Enable AI search on MkDocs sites by converting HTML to markdown and R markdown, leveraging Lunr.js for powerful md forma
Enables AI to search and retrieve content from MkDocs documentation sites by leveraging existing Lunr.js indexes and converting HTML to markdown for seamless integration.
best for
- / AI agents needing to search technical documentation
- / Developers working with MkDocs sites
- / Documentation analysis and content retrieval
capabilities
- / Search MkDocs documentation sites
- / Fetch and convert doc pages to markdown
- / Filter results by confidence threshold
- / Extract code examples with language detection
- / Preserve Mermaid diagrams
- / Cache search indexes and converted content
what it does
Searches and retrieves content from any MkDocs documentation site using the site's existing Lunr.js search index and converts pages to markdown.
about
MkDocs Search is a community-built MCP server published by serverless-dna that provides AI assistants with tools and capabilities via the Model Context Protocol. Enable AI search on MkDocs sites by converting HTML to markdown and R markdown, leveraging Lunr.js for powerful md forma It is categorized under search web, developer tools. This server exposes 2 tools that AI clients can invoke during conversations and coding sessions.
how to install
You can install MkDocs Search in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
MIT
MkDocs Search is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
MkDocs MCP Search Server
A Model Context Protocol (MCP) server that provides search functionality for any MkDocs powered site. This server relies on the existing MkDocs search implementation using the Lunr.Js search engine.
Claude Desktop Quickstart
Follow the installation instructions please follow the Model Context Protocol Quickstart For Claude Desktop users. You will need to add a section tothe MCP configuration file as follows:
{
"mcpServers": {
"my-docs": {
"command": "npx",
"args": [
"-y",
"@serverless-dna/mkdocs-mcp",
"https://your-doc-site",
"Describe what you are enabling search for to help your AI Agent"
]
}
}
}
Overview
This project implements an MCP server that enables Large Language Models (LLMs) to search through any published mkdocs documentation site. It uses lunr.js for efficient local search capabilities and provides results that can be summarized and presented to users.
Features
- MCP-compliant server for integration with LLMs
- Local search using lunr.js indexes
- Version-specific documentation search capability
- MkDocs Material HTML to Markdown conversion with structured JSON responses
- Code example extraction with language detection and context
- Tab view support for multi-language documentation
- Mermaid diagram preservation
- Automatic URL resolution (relative to absolute)
- Intelligent caching for both search indexes and converted documentation
Installation
# Install dependencies
pnpm install
# Build the project
pnpm build
Usage
The server can be run as an MCP server that communicates over stdio:
npx -y @serverless-dna/mkdocs-mcp https://your-doc-site.com
Available Tools
Search Tool
The server provides a searchMkDoc tool with the following parameters:
search: The search query stringversion: Optional version string (only for versioned sites)
Sample Response:
{
"query": "logger",
"version": "latest",
"total": 3,
"results": [
{
"title": "Logger",
"url": "https://docs.example.com/latest/core/logger/",
"score": 1.2,
"preview": "Logger utility for structured logging...",
"location": "core/logger/"
},
{
"title": "Configuration",
"url": "https://docs.example.com/latest/core/logger/#config",
"score": 0.8,
"preview": "Configure the logger with custom settings...",
"location": "core/logger/#config",
"parentArticle": {
"title": "Logger",
"location": "core/logger/",
"url": "https://docs.example.com/latest/core/logger/"
}
}
]
}
Features:
- Confidence-based filtering (configurable threshold)
- Advanced scoring with title matching and boosting
- Parent article context for section results
- Limited to top results (configurable, default: 10)
Fetch Documentation Tool
The server provides a fetchMkDoc tool that retrieves and converts documentation pages:
url: The URL of the documentation page to fetch
Sample Response:
{
"title": "Getting Started",
"markdown": "# Getting Started
This guide will help you...
## Installation
```bash
npm install example
```",
"code_examples": [
{
"title": "Installation",
"description": "Install the package using npm",
"code": "```bash
npm install example
```"
},
{
"title": "Basic Usage",
"description": "Import and initialize the library",
"code": "```python
from example import Client
client = Client()
```"
}
],
"url": "https://docs.example.com/getting-started/"
}
Configuration
The server can be configured using environment variables:
SEARCH_CONFIDENCE_THRESHOLD: Minimum confidence score for search results (default:0.1)SEARCH_MAX_RESULTS: Maximum number of search results to return (default:10)CACHE_BASE_PATH: Base directory for cache storage (default:<system-tmp>/mkdocs-mcp-cache)
Example:
SEARCH_MAX_RESULTS=20 SEARCH_CONFIDENCE_THRESHOLD=0.2 npx @serverless-dna/mkdocs-mcp https://your-doc-site.com
Cache Location: By default, the server caches search indexes and converted documentation in the system's temporary directory:
- macOS/Linux:
/tmp/mkdocs-mcp-cache(or$TMPDIR) - Windows:
%TEMP%\mkdocs-mcp-cache
You can override this with the CACHE_BASE_PATH environment variable.
Development
Building
pnpm build
Testing
pnpm test
Claude Desktop MCP Configuration
During development you can run the MCP Server with Claude Desktop using the following configuration.
The configuration below shows running in windows claude desktop while developing using the Windows Subsystem for Linux (WSL). Mac or Linux environments you can run in a similar way.
The output is a bundled file which enables Node installed in windows to run the MCP server since all dependencies are bundled.
{
"mcpServers": {
"powertools": {
"command": "node",
"args": [
"\\wsl$\Ubuntu\home\walmsles\dev\serverless-dna\mkdocs-mcp\dist\index.js",
"Search online documentation"
]
}
}
}
How It Works
Search Functionality
- The server loads pre-built lunr.js indexes for each supported runtime
- When a search request is received, it:
- Loads the appropriate index based on version (currently fixed to latest)
- Performs the search using lunr.js
- Returns the search results as JSON
- The LLM can then use these results to find relevant documentation pages
Documentation Fetching
- When a fetch request is received with a URL:
- Fetches the HTML content (with caching)
- Parses the MkDocs Material HTML structure using Cheerio
- Removes navigation, headers, footers, and other UI elements
- Processes tab views into sequential sections
- Extracts code blocks with language detection and context
- Resolves all relative URLs to absolute URLs
- Converts the cleaned HTML to markdown
- Returns a structured JSON response with title, markdown, and code examples
- Results are cached to improve performance on subsequent requests
License
MIT
FAQ
- What is the MkDocs Search MCP server?
- MkDocs Search is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for MkDocs Search?
- This profile displays 10 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.
Ratings
4.5★★★★★10 reviews- ★★★★★Shikha Mishra· Oct 10, 2024
MkDocs Search is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Piyush G· Sep 9, 2024
We evaluated MkDocs Search against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Chaitanya Patil· Aug 8, 2024
Useful MCP listing: MkDocs Search is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Sakshi Patil· Jul 7, 2024
MkDocs Search reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Ganesh Mohane· Jun 6, 2024
I recommend MkDocs Search for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Oshnikdeep· May 5, 2024
Strong directory entry: MkDocs Search surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Dhruvi Jain· Apr 4, 2024
MkDocs Search has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Rahul Santra· Mar 3, 2024
According to our notes, MkDocs Search benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Pratham Ware· Feb 2, 2024
We wired MkDocs Search into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Yash Thakker· Jan 1, 2024
MkDocs Search is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.