dbt-mcp

<instructions> Retrieves information about the health of a dbt model, including the last time it ran, the last test execution status, and whether the upstream data for the model is fresh. IMPORTANT: Use uniqueId when available. - Using uniqueId guarantees the correct model is retrieved - Using only model_name may return incorrect results or fail entirely - If you obtained models via get_all_models(), you should always use the uniqueId from those results ASSESSING MODEL HEALTH: For all of the below, summarize whether the model is healthy, questionable, or unhealthy. Only provide more details when asked. - for the model executionInfo, if the lastRunStatus is "success" consider the model healthy - for the test executionInfo, if the lastRunStatus is "success" consider the model healthy - for the models parents: -- check the modelexecutionInfo, snapshotExecutionInfo, and seedExecutionInfo. If the lastRunStatus is "success" consider the model healthy. If the lastRunStatus is "error" consider the model unhealthy. -- if the parent node is a SourceAppliedStateNestedNode: --- If the freshnessStatus is "pass", consider the model healthy --- If the freshnessStatus is "fail", consider the model unhealthy --- If the freshnessStatus is null, consider the model health questionable --- If the freshnessStatus is "warn", consider the model health questionable </instructions> <parameters> uniqueId: The unique identifier of the model (format: "model.project_name.model_name"). STRONGLY RECOMMENDED when available. model_name: The name of the dbt model. Only use this when uniqueId is unavailable. </parameters> <examples> 1. PREFERRED METHOD - Using uniqueId (always use this when available): get_model_details(uniqueId="model.my_project.customer_orders") 2. FALLBACK METHOD - Using only model_name (only when uniqueId is unknown): get_model_details(model_name="customer_orders") </examples>