MCP Connector

Automate Documentation Deployment

MCP server that analyzes a repo, recommends a static site generator, structures docs with Diataxis, and deploys them to GitHub Pages automatically.

Works with github

91
Spark score
out of 100
Updated last month
Source checked Sep 10, 2026
Version 1.0.0
Models
universal

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Why it matters

Streamline your open-source project's documentation by intelligently analyzing your repository, recommending the best static site generator, and automating deployment to GitHub Pages.

Outcomes

What it gets done

01

Analyze repository structure and dependencies.

02

Recommend optimal static site generators (Jekyll, Hugo, Docusaurus, MkDocs, Eleventy).

03

Automate GitHub Pages deployment with verification.

04

Generate documentation following the Diataxis framework.

Install

Add it to your toolbox

Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/vb-documcp | bash

After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.

Reports

Agent outcome reports

No reports yet

Capabilities

Tools your agent gets

analyze_repository

Deep analysis of repository structure and dependencies

recommend_ssg

Smart SSG recommendations with historical data and user preferences

detect_gaps

Identify missing documentation sections

deploy_pages

Automated GitHub Pages deployment with results tracking

verify_deployment

Comprehensive deployment validation

analyze_deployments

Analytics and insights from deployment history

manage_preferences

Manage user preferences for personalized recommendations

Overview

DocuMCP MCP Server

An MCP server that analyzes a repository, recommends a static site generator such as Jekyll, Hugo, Docusaurus, MkDocs, or Eleventy, scaffolds documentation following the Diataxis framework, and deploys it to GitHub Pages, with 30+ tools covering analysis, recommendations, deployment, and learning from past outcomes. Use it when a repository needs a documentation site built and deployed with minimal manual setup, such as repository analysis, generator selection, Diataxis structuring, and GitHub Pages deployment, rather than configuring each step by hand.

What it does

An MCP server that automates documentation deployment for open-source projects end to end: deep repository analysis, data-driven static site generator recommendations across Jekyll, Hugo, Docusaurus, MkDocs, and Eleventy, automatic documentation structure following the Diataxis framework (tutorials, how-to guides, reference, explanation), and automated GitHub Pages deployment with verification. A single prompt like analyze my repository and deploy documentation can drive the whole workflow. Beyond the core pipeline, it layers in historical intelligence that learns from past deployments, personalized recommendations based on user preferences and deployment analytics, documentation freshness tracking and sitemap management, and optional AI-powered semantic code analysis via DeepSeek, OpenAI, Anthropic, or Ollama that can detect behavioral changes beyond syntax and validate that documented code examples actually work.

When to use - and when NOT to

Use it to bootstrap or maintain a documentation site for an open-source project - getting an SSG recommendation grounded in what worked for similar projects rather than a guess, generating a proper Diataxis structure instead of an ad hoc docs folder, and deploying to GitHub Pages or Vercel with tracked outcomes. The LLM-powered semantic analysis features are entirely optional: the server works fully offline with AST-only analysis by default, and gracefully falls back to that mode if no LLM is configured, so privacy-sensitive or air-gapped projects lose semantic depth but not core functionality. It is specifically shaped around GitHub Pages and Vercel-style static documentation deployment, not a general web hosting or CMS tool.

Capabilities

Analysis and recommendations: analyze_repository (structure and dependency analysis), recommend_ssg (SSG recommendation using historical data and preferences), detect_gaps (missing documentation sections). Deployment and tracking: deploy_site, deploy to GitHub Pages or Vercel with outcome tracking, with deploy_pages remaining as a backward-compatible alias, verify_deployment, analyze_deployments (analytics from deployment history). Preferences and learning: manage_preferences, plus historical success-rate and similar-project recommendations. Over 30 tools total cover this pipeline plus content generation, validation, and memory-enhanced insights.

How to install

Requires Node.js 20+.

npm install -g documcp

Configure Claude Desktop:

{
  "mcpServers": {
    "documcp": {
      "command": "npx",
      "args": ["documcp"]
    }
  }
}

Or via Claude Code CLI: claude mcp add documcp -- npx documcp. Equivalent configs are documented for Cursor and VS Code with GitHub Copilot.

Who it's for

Open-source maintainers who want a documentation site analyzed, structured, and deployed with minimal manual setup, and teams that want deployment outcomes tracked and recommendations that improve as more projects deploy through it. The project is licensed under MIT.

Source README

DocuMCP - Intelligent Documentation Deployment MCP Server

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DocuMCP is an intelligent Model Context Protocol (MCP) server that revolutionizes documentation deployment for open-source projects. It provides deep repository analysis, intelligent static site generator recommendations, and automated GitHub Pages deployment workflows.

TL;DR

DocuMCP analyzes your repository, recommends the perfect static site generator (Jekyll, Hugo, Docusaurus, MkDocs, or Eleventy), creates professional documentation structure following Diataxis principles, and deploys it automatically to GitHub Pages. Just say "analyze my repository and deploy documentation" to get started.

Features

Core Capabilities

  • 🔍 Repository Analysis: Deep multi-layered analysis of project structure, dependencies, and documentation needs
  • 🎯 SSG Recommendations: Data-driven recommendations for Jekyll, Hugo, Docusaurus, MkDocs, or Eleventy
  • 📚 Diataxis Framework: Automatic creation of well-structured documentation following proven principles
  • 🚀 GitHub Pages Deployment: Automated workflow generation with SSG-specific optimizations
  • Deployment Verification: Comprehensive checks and troubleshooting for successful deployments

Intelligence & Learning (Phase 2)

  • 🧠 Historical Intelligence: Learns from past deployments to improve recommendations
  • 👤 User Preferences: Personalized recommendations based on your preferences and patterns
  • 📊 Deployment Analytics: Comprehensive insights into deployment patterns and success rates
  • 🎯 Smart Scoring: Intelligent SSG scoring based on success rates from similar projects
  • 📈 Trend Analysis: Identifies deployment trends and provides health scores

Documentation Maintenance (v0.5.2+)

  • 📅 Freshness Tracking: Monitor documentation staleness with configurable thresholds
  • Freshness Validation: Initialize and update freshness metadata automatically
  • 🗺️ Sitemap Management: Generate, validate, and manage sitemap.xml for SEO
  • 🔗 Knowledge Graph Integration: Track freshness history for intelligent recommendations

AI-Powered Semantic Analysis (v0.6.0+)

  • 🤖 LLM Integration: Optional integration with DeepSeek, OpenAI, Anthropic, or Ollama
  • 🔍 Semantic Code Analysis: Detect behavioral changes beyond syntax using AI
  • 🧪 Example Validation: Simulate code execution to verify documentation examples
  • 🎯 Intelligent Fallback: Graceful degradation to AST-only analysis when LLM unavailable
  • 🔒 Privacy First: Works fully offline with AST analysis, LLM completely optional

Requirements

  • Node.js: 20.0.0 or higher
  • npm: Latest stable version

Installation

Option A - npm global install (recommended):

npm install -g documcp

Option B - build from source (contributors / local dev):

git clone https://github.com/tosin2013/documcp.git
cd documcp
npm install
npm run build

MCP Client Setup

DocuMCP works with any MCP-enabled AI client. Pick the one you use:

Claude Desktop

File location:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json
  • Linux: ~/.config/claude/claude_desktop_config.json
{
  "mcpServers": {
    "documcp": {
      "command": "npx",
      "args": ["documcp"]
    }
  }
}

Restart Claude Desktop to load the configuration.

Claude Code (CLI)

# npm global install
claude mcp add documcp -- npx documcp

# local build (replace path as needed)
claude mcp add documcp -- node /path/to/documcp/dist/index.js

Verify with claude mcp list - no restart required.

Cursor

File location: ~/.cursor/mcp.json (or Settings > MCP in the UI)

{
  "mcpServers": {
    "documcp": {
      "command": "npx",
      "args": ["documcp"]
    }
  }
}

VS Code (GitHub Copilot)

Add to settings.json (Cmd/Ctrl + Shift + P → Preferences: Open User Settings (JSON)):

{
  "mcp.servers": {
    "documcp": {
      "command": "npx",
      "args": ["documcp"]
    }
  }
}

Troubleshooting

  • Ensure npx is available in your PATH
  • To find a global install path: npm list -g documcp
  • To use the full path instead of npx:
    {
      "command": "node",
      "args": ["/usr/local/lib/node_modules/documcp/dist/index.js"]
    }
    

Quick Start

Once connected to your AI client, use natural language prompts:

Analyze my repository at /path/to/my-project
Recommend a static site generator based on my analysis
Set up a Diataxis documentation structure using Docusaurus
Deploy my documentation to GitHub Pages

DocuMCP provides 30+ tools including repository analysis, intelligent SSG recommendations, content generation, deployment automation with tracking, validation, user preference management, deployment analytics, and memory-enhanced insights. See the complete documentation for detailed tool reference.

Key Tools

Analysis & Recommendations

  • analyze_repository - Deep repository structure and dependency analysis
  • recommend_ssg - Intelligent SSG recommendations with historical data and user preferences
  • detect_gaps - Identify missing documentation sections

Deployment & Tracking

  • deploy_site - Automated deployment to GitHub Pages or Vercel with outcome tracking (deploy_pages still works as a back-compat alias)
  • verify_deployment - Comprehensive deployment validation
  • analyze_deployments - Analytics and insights from deployment history

User Preferences & Learning

  • manage_preferences - Manage user preferences for personalized recommendations
  • View historical success rates and deployment patterns
  • Get recommendations based on similar projects' success

Development

# Run in development mode
npm run dev

# Run tests
npm test

# Lint code
npm run lint

# Type check
npm run typecheck

Architecture

DocuMCP follows a modular, stateless architecture:

  • TypeScript-based implementation using the official MCP SDK
  • Stateless operation for consistency and reliability
  • Modular design with clear separation of concerns
  • Progressive complexity allowing users to start simple

Documentation Structure (Diataxis)

DocuMCP automatically creates documentation following the Diataxis framework:

  • Tutorials: Learning-oriented guides for newcomers
  • How-To Guides: Task-oriented recipes for specific goals
  • Reference: Information-oriented technical descriptions
  • Explanation: Understanding-oriented conceptual discussions

Security

Please see our Security Policy for reporting vulnerabilities and security-related issues.

FAQ

Common questions

Discussion

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