dbt Discovery Tools

The dbt Discovery Tools let DinoAI answer questions about your dbt project’s structure, health, and performance from metadata, without reading files or querying the warehouse. They are backed by the artifacts of your Bolt runs, the same data behind the Discovery API. Tool names, parameters, and output mirror the dbt-mcp Discovery toolset, so prompts written against dbt-mcp work unchanged.

Capabilities

Tool	What it returns
`get_all_models`	Name, unique ID, and description of every model
`get_mart_models`	Mart models only (`fct_`/`dim_` prefixes or a `marts/` path)
`get_all_sources`	Sources with their freshness status, filterable by source name
`get_all_macros`	Macros, with dbt Labs builtin packages excluded by default
`get_exposures`	Downstream dashboards, apps, and analyses
`get_lineage`	The lineage graph around a node, filterable by type, depth, and direction
`get_model_health`	A model’s last run status, test statuses, and upstream parents’ status and freshness
`get_model_performance`	Historical run timings for a model, newest first, with optional per-run test results
`get_node_details`	Full details for any node: model, source, exposure, test, seed, snapshot, or macro

All nine tools are read-only and run without prompting for approval.

Discovery data comes from your Bolt runs in the workspace’s default Bolt environment. If a workspace has no Bolt runs with dbt™ artifacts yet, the tools return an error explaining that no discovery data has been ingested.

Example Use Cases

Checking model health before a change Prompt

Is stg_orders healthy? Check its tests and upstream sources.

Result: DinoAI reports the model’s last run status, each attached test’s status, and whether its direct parents ran successfully and have fresh sources. Investigating slow runs Prompt

How long did fct_orders take over the last 10 runs?

Result: DinoAI returns the model’s historical execution times so you can spot regressions.

Working with Other Tools

Combine with the Catalog Tool to first find an asset by business concept, then pull its full details and lineage with get_node_details and get_lineage
Combine with the SQL Execution Tool to validate the data behind a model whose metadata looks suspicious
Combine with the Bolt Logs Tool to jump from a failing model’s health report to the logs of the run that broke it

Slack Bot Agent

Programmable Agent

Bolt Pipeline Agent

Copilot

MCP Server

Context & Customization

DinoAI Tools

Settings & Credits

dbt Discovery Tools

Capabilities

Example Use Cases

Working with Other Tools

​Capabilities

​Example Use Cases

​Working with Other Tools

Capabilities

Example Use Cases

Working with Other Tools