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The Data Warehouse Tools allow DinoAI to explore your connected warehouse metadata directly from the Code IDE. DinoAI can inspect databases, schemas, tables, columns, and relationships so it can write more accurate SQL, generate dbt assets faster, and reason about your warehouse structure with real context.
Metadata only. These tools inspect schema metadata, not table contents. If you need DinoAI to run queries against actual data, use the SQL Execution Tool.

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Capabilities

The Data Warehouse Tools give DinoAI the ability to:
  • Discover available databases, catalogs, schemas, and tables
  • Inspect column names, data types, descriptions, and nested fields where supported
  • Understand relationships between warehouse objects
  • Generate accurate dbt source definitions and model references
  • Suggest join conditions based on real warehouse structure
  • Help investigate warehouse-specific metadata such as query performance where supported

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Supported Data Warehouses

Each warehouse has its own toolset and capabilities:
Requires a warehouse connection. These tools are available when your workspace has a supported warehouse configured. See Code IDE environment to set up a connection.

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Using the Data Warehouse Tools

  1. Open DinoAI in the right panel of the Code IDE
  2. Describe the warehouse object you want to inspect
  3. Add your prompt describing what DinoAI should do with that metadata
  4. Grant permission when DinoAI asks to access your warehouse connection
  5. Review the results and apply DinoAI’s suggested changes

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Example Use Cases

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Generating dbt Source Definitions

Prompt
Result: DinoAI inspects the schema metadata and generates source definitions with the correct table and column names.

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Understanding an Unfamiliar Schema

Prompt
Result: DinoAI explores the warehouse structure, summarizes the main tables, and highlights likely joins and dependencies.

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Building a Model with Accurate References

Prompt
Result: DinoAI uses the real column definitions to create a model with accurate references and cleaner naming.

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Working with Other Tools

The Data Warehouse Tools work well alongside DinoAI’s other capabilities:
  • Combine with the SQL Execution Tool to validate assumptions against live query results
  • Combine with the Column Level Lineage tool to trace how warehouse columns flow through downstream dbt assets
  • Use with .dinorules to define persistent modeling and naming conventions for DinoAI

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Best Practices

  • Be specific about schemas when working in large warehouses
  • Provide full object names when possible to reduce extra lookups
  • Mention the warehouse platform if your workspace uses multiple connections
  • Start with exploration before asking DinoAI to generate models or sources
  • Use the warehouse-specific pages above for features unique to each platform

Related topics

Tools and FeaturesDuckDB ToolMCP ServerData Warehouse essentialsSetting Up Data Warehouse Connections