Which integrations are available for this server?

Provides [dbt](/mcp/servers/integrations/dbt) CLI operations including running, testing, compiling, and building models, along with project discovery, metadata analysis, and lineage exploration for dbt Core data transformation projects. Enables direct SQL execution against [DuckDB](/mcp/servers/integrations/duckdb) warehouses, allowing for database querying and interaction with DuckDB analytical databases.

Modern Data Stack with Meltano, DuckDB, and dbt Core

A production-ready data stack implementation combining Meltano for ELT orchestration, DuckDB for high-performance analytics, and dbt Core for data transformations.

Architecture Overview

CSV Data → Meltano (Extract/Load) → DuckDB → dbt Core (Transform) → Analytics Tables

Data Flow:

Extract: Meltano's tap-csv reads sample employee data
Load: target-duckdb loads raw data into DuckDB
Transform: dbt models create staging views and analytics tables
Validate: Data quality tests ensure integrity

Related MCP server: MCP Code Analyzer

Tool Versions (Latest Compatible)

Meltano 3.8.0 - DataOps platform for ELT pipelines
DuckDB 1.3.2 - High-performance in-process analytics database
dbt Core 1.10.4 - Data transformation framework
dbt-duckdb 1.9.4 - DuckDB adapter for dbt

Quick Start

1. Environment Setup

# Clone and navigate to project cd claude-data-stack-mcp # Create and activate virtual environment python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate # Install all dependencies pip install -r requirements.txt

2. Run Complete Pipeline

# Step 1: Extract and Load with Meltano meltano run tap-csv target-duckdb # Step 2: Transform with dbt cd transform DBT_PROFILES_DIR=./profiles/duckdb dbt run --profile data_stack --project-dir . # Step 3: Validate with data quality tests DBT_PROFILES_DIR=./profiles/duckdb dbt test --profile data_stack --project-dir .

3. Verify Results

# Check transformed data python -c " import duckdb conn = duckdb.connect('data/warehouse/data_stack.duckdb') print('=== Department Stats ===') result = conn.execute('SELECT * FROM main.agg_department_stats').fetchall() for row in result: print(row) "

Project Structure

├── data/ │ ├── sample_data.csv # Sample employee data │ └── warehouse/ # DuckDB database files ├── transform/ # dbt project │ ├── models/ │ │ ├── staging/ # Staging models │ │ │ ├── stg_employees.sql │ │ │ └── sources.yml │ │ └── marts/ # Analytics models │ │ ├── dim_employees.sql │ │ └── agg_department_stats.sql │ ├── profiles/duckdb/ # Project-contained profiles │ └── dbt_project.yml ├── meltano.yml # Meltano configuration ├── requirements.txt # Python dependencies └── README.md # This file

Data Models

Staging Layer

stg_employees: Clean, typed employee data from raw CSV

Analytics Layer

dim_employees: Employee dimension with salary tiers (Junior/Mid-Level/Senior)
agg_department_stats: Department-level aggregations (count, avg/min/max salary, total payroll)

Data Quality Tests

Unique constraints: Employee IDs must be unique
Not null constraints: Employee IDs cannot be null

Usage Examples

Add New Data Sources

# Browse available extractors meltano discover extractors # Add a new extractor (e.g., PostgreSQL) meltano add extractor tap-postgres # Configure in meltano.yml and run meltano run tap-postgres target-duckdb

Create New dbt Models

-- transform/models/marts/new_model.sql {{ config(materialized='table') }} select department, count(*) as employee_count, avg(annual_salary) as avg_salary from {{ ref('stg_employees') }} group by department

Development Workflow

# Test individual dbt models cd transform DBT_PROFILES_DIR=./profiles/duckdb dbt run --models stg_employees --profile data_stack --project-dir . # Run only marts models DBT_PROFILES_DIR=./profiles/duckdb dbt run --models marts --profile data_stack --project-dir . # Generate documentation DBT_PROFILES_DIR=./profiles/duckdb dbt docs generate --profile data_stack --project-dir .

Configuration

Meltano Configuration

Extractor: tap-csv configured for data/sample_data.csv
Loader: target-duckdb configured for data/warehouse/data_stack.duckdb
Environments: dev, staging, prod

dbt Configuration

Profile: data_stack with project-contained profiles
Target: DuckDB database in data/warehouse/
Materializations: Views for staging, tables for marts

Troubleshooting

Common Issues

"Table does not exist" errors:

Ensure Meltano ELT step completed successfully
Check data/warehouse/data_stack.duckdb exists

dbt profile errors:

Verify you're in the transform/ directory
Use DBT_PROFILES_DIR=./profiles/duckdb flag

Python dependency conflicts:

Use fresh virtual environment
Ensure Python 3.13+ compatibility

Validation Commands

# Check Meltano configuration meltano config list # Validate dbt setup cd transform DBT_PROFILES_DIR=./profiles/duckdb dbt debug --profile data_stack # Inspect DuckDB directly python -c "import duckdb; conn = duckdb.connect('data/warehouse/data_stack.duckdb'); print(conn.execute('SHOW TABLES').fetchall())"

Next Steps

Add More Data Sources: Integrate APIs, databases, or files using Meltano's extensive extractor library
Expand Transformations: Create more sophisticated dbt models for advanced analytics
Add Orchestration: Integrate with Airflow, Prefect, or other orchestration tools
Enable Monitoring: Add data quality monitoring and alerting
Scale Storage: Migrate to cloud data warehouses (Snowflake, BigQuery, etc.)

MCP Integration

NEW: Claude Code MCP server for intelligent dbt assistance!

# Start dbt MCP server for Claude Code integration ./scripts/start_dbt_mcp.sh

Capabilities:

dbt CLI Operations: dbt_run, dbt_test, dbt_compile, dbt_build
Project Discovery: Model listing, metadata analysis, lineage exploration
Database Querying: Direct SQL execution against DuckDB warehouse
Real-time Assistance: Context-aware dbt project support

Documentation:

🚀 Quick Start Guide - 5-minute setup
📚 API Reference - Complete tool documentation
💡 Usage Examples - Practical workflows
🔧 Integration Guide - Detailed configuration

Status: ✅ Production Ready + MCP Enhanced

This data stack has been systematically implemented and validated through comprehensive testing. All components are using the latest compatible versions and following best practices. Enhanced with Claude Code MCP integration for intelligent development assistance.

dbt MCP Server