Allows for crawling and indexing documentation from GitHub repositories to provide AI agents with a searchable knowledge base of tool documentation.
Uses OpenAI's text-embedding models to enable semantic search and natural language understanding of documentation content.
Integrates with Upstash Vector to store and query vectorized documentation, facilitating efficient semantic retrieval for AI agents.
Click on "Install 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., "@Docs Vector MCP search the docs for how to index a new repository"
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.
Vectorize GitHub tool documentation and provide MCP (Model Control Protocol) interface for AI Agents.
🔄 Auto-fetch from GitHub - Automatically crawls and extracts documentation from GitHub repositories
🧠 Vector Embeddings - Uses OpenAI embeddings to store documentation in vector database
🔍 Semantic Search - Find relevant documentation using natural language queries
🔌 MCP Protocol - Standard Model Control Protocol interface for AI Agents
🎨 Modern Web UI - Built with Next.js 15 + TailwindCSS
Related MCP server: Gread
┌─────────────┐ ┌──────────────┐ ┌──────────────┐ ┌────────────┐
│ GitHub Repo │ → │ Crawl Docs │ → │ Split Chunks│ → │ Embedding │
└─────────────┘ └──────────────┘ └──────────────┘ └────────────┘
↓
┌──────────────┐
│ Vector DB │ ← Query ┌──────────┐
│ (Upstash) │ → Result │ AI Agent │
└──────────────┘ └──────────┘
↑
┌───────────┐
│ MCP API │
└───────────┘Framework: Next.js 15 + TypeScript + TailwindCSS
Vector Database: Upstash Vector (serverless, perfect for Cloudflare deployment)
Embeddings: OpenAI text-embedding-3-small
GitHub API: Octokit
MCP: @modelcontextprotocol/sdk
Create a .env.local file:
# GitHub (optional but recommended for higher rate limits)
GITHUB_TOKEN=your_github_token
# OpenAI
OPENAI_API_KEY=your_openai_api_key
# Upstash Vector
UPSTASH_VECTOR_RESTAR_URL=your_upstash_vector_url
UPSTASH_VECTOR_RESTAR_TOKEN=your_upstash_vector_tokennpm installnpm run devOpen http://localhost:3000 in your browser.
npx tsx cli/index.ts index <owner> <repo> [branch]Example:
npx tsx cli/index.ts index openai openai-python mainnpx tsx cli/index.ts search "how to use embeddings"npx tsx cli/index.ts statsnpx tsx cli/index.ts clearnpx tsx cli/index.ts mcpAdd this configuration to your AI Agent that supports MCP:
{
"mcpServers": {
"docs-vector": {
"command": "node",
"args": [
"path/to/docs-vector-mcp/dist/cli/index.js",
"mcp"
],
"env": {
"OPENAI_API_KEY": "<your-openai-api-key>",
"UPSTASH_VECTOR_RESTAR_URL": "<your-upstash-url>",
"UPSTASH_VECTOR_RESTAR_TOKEN": "<your-upstash-token>"
}
}
}
}search_docs - Search documentation semantically
Parameters:
query (string): The search query
limit (number, optional): Maximum number of results (1-20, default 5)
get_stats - Get statistics about stored documentation
No parameters
This project is optimized for Cloudflare Pages deployment:
Push your code to GitHub
Connect your repository to Cloudflare Pages
Set build command: npm install && npx next build
Set output directory: .next
Add all environment variables in Cloudflare dashboard
Deploy!
A sample workflow is included in .github/workflows/deploy.yml that automatically deploys to Cloudflare Pages on every push to main branch.
docs-vector-mcp/
├── app/ # Next.js app router
│ ├── api/ # API routes
│ │ ├── index/ # Indexing endpoint
│ │ ├── search/ # Search endpoint
│ │ └── stats/ # Stats endpoint
│ ├── globals.css # Global styles
│ ├── layout.tsx # Root layout
│ └── page.tsx # Home page
├── components/ # React components
│ ├── IndexForm.tsx # Repository indexing form
│ └── SearchForm.tsx # Search form
├── lib/ # Core libraries
│ ├── github.ts # GitHub fetcher
│ ├── text-processor.ts # Text chunking
│ ├── embedding.ts # Embedding generator
│ ├── vector-store.ts # Vector storage
│ ├── mcp-server.ts # MCP server
│ └── docs-service.ts # Service orchestrator
├── cli/ # CLI entry
│ └── index.ts # CLI main
├── .github/
│ └── workflows/ # GitHub Actions
├── next.config.ts # Next.js config
├── tailwind.config.ts # Tailwind config
└── package.json # DependenciesAdd Repository: You input a GitHub repository that contains tool documentation
Crawling: The system fetches all documentation files (.md, .mdx, .rst, .txt, etc.) from the repo
Processing: Text is cleaned and split into overlapping chunks
Embedding: OpenAI generates vector embeddings for each chunk
Storage: Vectors are stored in Upstash Vector database
Search: When an AI Agent asks a question, the query is embedded and similar documents are retrieved
Response: Relevant documentation snippets are returned to the AI Agent for answering
MIT
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An MCP server that gives your AI access to the source code and docs of all public github repos
Local-first RAG engine with MCP server for AI agent integration.
Universal memory for AI agents and tools. Save, organize and search context anywhere.
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