Data Product Hub

NOTE: This project is still under construction and in a state of flux. It is being tested internally so setup instructions below may not work as intended.

Universal MCP Server for dbt Project Analysis - Works with Any GitHub Repository

A production-ready Model Context Protocol (MCP) server that provides comprehensive dbt project quality assessment for any GitHub repository. Powered by GitHub App authentication for secure, scalable access to public and private repositories. Purpose-built for AI agents and modern data workflows.

🚀 What is Data Product Hub?

Data Product Hub transforms any dbt project on GitHub into an agent-accessible data quality platform that:

• Analyzes ANY GitHub dbt repository with AI-powered suggestions and best practices

• Works with public and private repos via secure GitHub App authentication

• Supports subdirectory dbt projects (detects dbt/, transform/, analytics/ folders)

• Checks metadata coverage across your entire data product portfolio

• Maps data lineage and dependency relationships

• Integrates with Git for enhanced context and change analysis

• Exposes MCP tools for seamless AI agent integration

• Deploys anywhere - FastMCP Cloud (recommended), Docker, Kubernetes

Related MCP server: @us-all/dbt-mcp

Features

🔧 Universal MCP Tools (Work with Any GitHub Repository)

• analyze_dbt_model(model_name, repo_url) - Basic dbt model analysis

• analyze_dbt_model_with_ai(model_name, repo_url) - NEW: AI-powered analysis with user's OpenAI key

• check_metadata_coverage(repo_url) - Project-wide metadata assessment

• get_project_lineage(repo_url) - Data dependency mapping

• assess_data_product_quality(model_name, repo_url) - Comprehensive quality scoring

• validate_github_repository(repo_url) - Validate repo access and dbt structure

• analyze_dbt_model_with_git_context(model_name, repo_url) - dbt analysis + Git history

• get_composite_server_status() - Server capabilities and GitHub integration status

🌐 Deployment Flexibility

• Local CLI - dph -f ./project

• Hostable MCP Server - dph serve --mcp-host 0.0.0.0

• Container Deployment - Docker + Kubernetes + Helm charts

• FastMCP Cloud - One-click cloud deployment

🔗 Agent Integration

• Compatible with Claude Code, Cursor, and any MCP-enabled AI agent

• JSON-first output for automation and CI/CD pipelines

• Structured responses for programmatic consumption

Quick Start

🎯 GitHub Repository Analysis (Recommended)

1. Install the GitHub App on your dbt repositories:

• Visit: https://github.com/apps/data-product-hub/installations/new

• Select repositories containing dbt projects

• Grant read permissions

2. (Optional) Enable AI features by adding your OpenAI API key:

• Go to Repository Settings → Environments

• Create or use any of these environment names: production, prod, data-analysis, main, or staging

• Add OPENAI_API_KEY as an Environment Secret

• Set the value to your OpenAI API key (sk-proj-...)

• This enables the analyze_dbt_model_with_ai tool

• Note: All other tools work without an API key - only AI-powered analysis requires it

3. Use via Claude Desktop:

// Add to ~/.claude_desktop_config.json
{
  "mcpServers": {
    "data-product-hub": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-fetch", "https://data-product-hub.fastmcp.app/mcp"]
    }
  }
}

4. Ask Claude to analyze any dbt repository:

"Analyze the customer_metrics model in https://github.com/company/analytics-dbt"
"Get AI-powered suggestions for the user_events model in github.com/company/dbt-models"
"Check metadata coverage for github.com/myorg/data-warehouse"
"Get project lineage for github.com/startup/dbt-models"

🖥️ Local CLI Usage (Backwards Compatible)

# Install package
pip install data-product-hub

# CLI analysis
dph -f ./my-dbt-project --metadata-only

# Start local MCP server
dph --mcp-server -f ./my-dbt-project

🔌 Programmatic Integration

from fastmcp import Client

# Connect to the universal MCP server
client = Client("https://data-product-hub.fastmcp.app/mcp")

async with client:
    # Basic analysis of any GitHub repository
    analysis = await client.call_tool(
        "analyze_dbt_model",
        {
            "model_name": "customer_summary",
            "repo_url": "https://github.com/company/analytics-dbt"
        }
    )

    # AI-powered analysis (requires OpenAI API key in environment secrets)
    ai_analysis = await client.call_tool(
        "analyze_dbt_model_with_ai",
        {
            "model_name": "customer_summary",
            "repo_url": "https://github.com/company/analytics-dbt"
        }
    )

    # Check metadata coverage across any project
    coverage = await client.call_tool(
        "check_metadata_coverage",
        {"repo_url": "github.com/myorg/data-warehouse"}
    )

Deployment Options

1. Use the Hosted Service (Recommended)

Ready to use immediately:

• MCP Server: https://data-product-hub.fastmcp.app/mcp

• GitHub App: https://github.com/apps/data-product-hub/installations/new

Quick Setup:

1. Install the GitHub App on your dbt repositories

2. Add the MCP server to Claude Desktop configuration

3. Start analyzing any dbt repository via Claude

2. Deploy Your Own Instance

For organizations wanting their own instance:

Prerequisites:

• Fork this repository

• Create your own GitHub App with read permissions

• Get GitHub App ID and base64-encoded private key

Deployment:

1. Deploy to FastMCP Cloud with entry point: server.py

2. Set your GitHub App credentials as environment variables

3. Share your GitHub App installation URL with users

📖 Complete Deployment Guide

2. Docker Deployment

# Using Docker Compose
docker-compose up

# Custom container
docker run -p 8080:8080 \
  -v ./my-dbt-project:/dbt-project \
  data-product-hub:latest

3. Kubernetes Deployment

# Deploy with Helm
helm install data-product-hub ./charts/data-product-hub \
  --set persistence.hostPath="/path/to/dbt-project" \
  --set dbtAi.database="snowflake"

📖 Full Kubernetes Guide

Configuration

The Data Product Hub MCP server is ready to use - no configuration required for end users! Just install the GitHub App and start analyzing.

For Local CLI Usage Only

# Database configuration (local CLI only)
DATABASE=snowflake  # snowflake, postgres, redshift, bigquery

# OpenAI API (optional - for AI features in local CLI)
OPENAI_API_KEY=your-openai-api-key
DBT_AI_BASIC_MODEL=gpt-4o-mini
DBT_AI_ADVANCED_MODEL=gpt-4o

Supported Databases

• Snowflake (default)

• PostgreSQL

• Amazon Redshift

• Google BigQuery

Architecture

Data Product Hub implements a composite MCP architecture:

Your Data Product Hub Server
├── Core dbt Analysis
├── Git Integration (via Git MCP server)
├── Future: Monte Carlo Integration
├── Future: DataHub Integration
└── Future: Snowflake Performance Integration

This allows AI agents to get comprehensive data product insights from a single MCP endpoint.

Use Cases

For Data Teams

• Automated quality checks in CI/CD pipelines

• Documentation coverage monitoring

• Lineage analysis for impact assessment

• Agent-driven data workflows

for AI Agents

• Data product understanding before making changes

• Quality assessment as part of automated reviews

• Context-aware suggestions with Git history

• Comprehensive data product insights

For Platform Teams

• Centralized data quality hub

• Production-ready MCP server deployment

• Multi-tool integration platform

• Kubernetes-native scaling

Migrating from dbt-ai

If you're upgrading from the legacy dbt-ai package:

# Old command
dbt-ai -f ./project --metadata-only

# New command (identical functionality) - use the short dph command!
dph -f ./project --metadata-only

All CLI functionality is 100% backwards compatible.

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

License

MIT License

Data Product Hub - Transforming dbt projects into agent-accessible data quality platforms. 🚀