Dbt Doctor

AI-driven quality and governance MCP Server for dbt projects.

Audit coverage, profile data, detect schema drift, and auto-generate documentation—all through natural language with an AI assistant.

What is dbt-doctor?

dbt-doctor is a Model Context Protocol (MCP) server that provides your AI coding assistant with deep context regarding your dbt project's health. Instead of manually running CLI commands and analyzing outputs, you can interact with your AI:

• "What's the overall health of my dbt project?"
• "Profile the fct_orders model and suggest appropriate tests."
• "Auto-document the models that have the lowest test coverage."

The tool handles the heavy operations—reading the manifest, profiling your data warehouse, detecting schema drift, and writing back to schema.yml files—without requiring you to leave the chat.

Note: This tool is designed to complement the official dbt-labs/dbt-mcp. While dbt-labs/dbt-mcp focuses on running dbt commands, dbt-doctor focuses on auditing, profiling, and documentation.

Key Features

Project Auditing

Evaluate your project with a 0–100% score based on documentation, testing, and naming conventions. Access a ranked list of models lacking coverage to prioritize your efforts.

Data Profiling

Perform efficient single-pass column statistics—including NULL rates, cardinality, min/max values, and uniqueness—using one batched SQL query per table to avoid slow row-by-row scanning.

Schema Drift Detection

Compare the current state of your data warehouse against the definitions in your manifest.json. Instantly identify added, removed, or type-changed columns.

Intelligent Test Suggestions

Translate profiling statistics into actionable dbt test recommendations. For example, a uniquely populated column without nulls will prompt suggestions for not_null and unique tests, while low cardinality will suggest accepted_values with predefined options.

Non-Destructive YAML Writing

Update schema.yml files using ruamel.yaml to retain hand-written comments, existing tests, and formatting. The tool only appends missing information and preserves your manual configurations.

End-to-End Documentation Generation

Execute a complete workflow in a single conversational turn: profile a model, suggest tests, preview changes, and write to schema.yml.

Included MCP Tools

| Category | Tool | Description | |---|---|---| | Context | list_models | Overview of all models and their coverage status | | Context | get_model_details | Detailed model information including SQL, columns, lineage, and tests | | Audit | audit_project | Project health score and naming convention violations | | Audit | check_test_coverage | Models ranked by their test coverage percentage | | Audit | analyze_dag | Detection of orphan models and high fan-out nodes | | Audit | get_project_health | Single-call dashboard summarizing project status | | Profiling | profile_model | Batched column statistics | | Profiling | execute_query | Read-only SQL execution against your warehouse | | Profiling | detect_schema_drift | Comparison of database columns against manifest definitions | | Generation | suggest_tests | Translation of profile data into dbt test recommendations | | Generation | `update_

... View full README on GitHub