MCP Jina Supabase RAG

A lean, focused MCP server for crawling documentation websites and indexing them to Supabase for RAG (Retrieval-Augmented Generation).

Features

• Smart URL Discovery: Tries sitemap.xml first, falls back to Crawl4AI recursive discovery
• Hybrid Content Extraction: Uses Jina AI for fast content extraction, Crawl4AI as fallback
• Multi-Project Support: Index multiple documentation sites to separate Supabase projects
• Efficient Chunking: Intelligent text chunking with configurable size and overlap
• Vector Embeddings: OpenAI embeddings stored in Supabase pgvector

Architecture

┌─────────────────────────────────────────────────────────────┐
│                    MCP Server Tools                         │
├─────────────────────────────────────────────────────────────┤
│  1. crawl_and_index(url_pattern, project_name)             │
│  2. list_projects()                                         │
│  3. search_documents(query, project_name, limit)           │
└─────────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────────┐
│                   Discovery Layer                           │
├─────────────────────────────────────────────────────────────┤
│  • Try sitemap.xml (fast)                                   │
│  • Try common doc patterns                                  │
│  • Crawl4AI recursive discovery (fallback)                  │
└─────────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────────┐
│                  Extraction Layer                           │
├─────────────────────────────────────────────────────────────┤
│  • Jina AI Reader API (primary, fast)                       │
│  • Crawl4AI (fallback for complex pages)                    │
└─────────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────────┐
│              Chunking & Embedding Layer                     │
├─────────────────────────────────────────────────────────────┤
│  • Smart text chunking                                      │
│  • OpenAI embeddings (text-embedding-3-small)               │
└─────────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────────┐
│                   Supabase Storage                          │
├─────────────────────────────────────────────────────────────┤
│  • pgvector for similarity search                           │
│  • Project isolation via source column                      │
└─────────────────────────────────────────────────────────────┘

Installation

Prerequisites

• Python 3.12+
• Supabase account
• OpenAI API key
• Jina AI API key (optional, recommended)

Setup

1. Clone the repository:

git clone https://github.com/yourusername/mcp-jina-supabase-rag.git
cd mcp-jina-supabase-rag

2. Install dependencies:

# Using uv (recommended)
uv venv
source .venv/bin/activate  # or .venv\Scripts\activate on Windows
uv pip install -e .

# Or using pip
pip install -e .

3. Set up Supabase database:

# Run the SQL in supabase_schema.sql in your Supabase SQL Editor

4. Configure environment:

cp .env.example .env
# Edit .env with your credentials

Usage

Running the MCP Server

# SSE transport (recommended for remote connections)
python src/main.py

# The server will start on http://localhost:8052/sse

Configure MCP Client

Claude Code

claude mcp add --transport sse jina-supabase http://localhost:8052/sse

Cursor / Claude Desktop

{
  "mcpServers": {
    "jina-supabase": {
      "transport": "sse",
      "url": "http://localhost:8052/sse"
    }
  }
}

Slash Command

Create /home/marty/.claude/commands/jina.md:

---
allowed-tools: mcp__jina-supabase
argument-hint: <url_pattern> <project_name>
description: Crawl documentation and index to Supabase RAG
---

# Index Documentation to Supabase

Use the jina-supabase MCP server to crawl and index documentation.

Arguments:
- $1: URL pattern (e.g., https://docs.example.com/*)
- $2: Project name for isolation

Example:
/jina https://docs.anthropic.com/claude/* anthropic-docs

Tools

crawl_and_index

Crawl a documentation site and index to Supabase.

Parameters:

• url_pattern (string): URL or pattern to crawl
• project_name (string): Project identifier for isolation
• discovery_method (string, optional): auto, sitemap, or crawl
• extraction_method (string, optional): auto, jina, or crawl4ai

Example:

await crawl_and_index(
    url_pattern="https://docs.supabase.com/docs/*",
    project_name="supabase-docs",
    discovery_method="auto",
    extraction_method="jina"
)

list_projects

List all indexed projects.

Returns: List of project names with document counts

search_documents

Search indexed documents using vector similarity.

Parameters:

• query (string): Search query
• project_name (string, optional): Filter by project
• limit (int, optional): Max results (default: 5)

Example:

results = await search_documents(
    query="How do I set up authentication?",
    project_name="supabase-docs",
    limit=10
)

Configuration

See .env.example for all configuration options.

Discovery Methods

• auto: Try sitemap first, fallback to crawl
• sitemap: Only use sitemap.xml (fast, fails if no sitemap)
• crawl: Only use Crawl4AI recursive discovery (slow, comprehensive)

Extraction Methods

• auto: Use Jina for bulk extraction (>10 URLs), Crawl4AI otherwise
• jina: Use Jina AI Reader API (fast, requires API key)
• crawl4ai: Use Crawl4AI browser automation (slow, no API key needed)

Development

# Install dev dependencies
uv pip install -e ".[dev]"

# Run tests
pytest

# Format code
black src/

# Lint
ruff check src/

Differences from mcp-crawl4ai-rag

Feature	mcp-crawl4ai-rag	mcp-jina-supabase-rag
Focus	Full-featured RAG with knowledge graphs	Lean documentation indexer
Discovery	Recursive only	Sitemap first, crawl fallback
Extraction	Crawl4AI only	Jina primary, Crawl4AI fallback
Dependencies	Heavy (Neo4j, etc.)	Light (core only)
Use Case	Advanced RAG with hallucination detection	Fast doc indexing

License

MIT

Contributing

Contributions welcome! Please open an issue first to discuss changes.