> For the complete documentation index, see [llms.txt](https://v2.dataos.info/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://v2.dataos.info/references/interfaces/ai-activation/data-product-mcp-tools.md).

# Data Product MCP tools

Data Product MCP exposes 18 tools that help the assistant discover, trust, query, build, and operate Data Products.

## Discovery and metadata

| Tool           | What it does                                                                                                  |
| -------------- | ------------------------------------------------------------------------------------------------------------- |
| `search`       | Searches Data Products, tables, columns, owners, metrics, lifecycle stage, PII, and related catalog context.  |
| `vulcan_about` | Returns product metadata such as description, domain, tags, terms, use cases, limitations, owner, and readme. |

## Querying and schema

| Tool                     | What it does                                                                                        |
| ------------------------ | --------------------------------------------------------------------------------------------------- |
| `vulcan_query`           | Executes a semantic query with measures, dimensions, filters, time dimensions, and pagination.      |
| `vulcan_semantic_schema` | Returns the queryable semantic layer: measures, dimensions, time dimensions, segments, and metrics. |

## Observability

| Tool             | What it does                                                                                                      |
| ---------------- | ----------------------------------------------------------------------------------------------------------------- |
| `vulcan_quality` | Assesses data quality with status, check-level drill-down, diagnostics, and run history.                          |
| `vulcan_runs`    | Checks run history, last run status, duration, per-model metrics, schedule, overdue flags, and diagnostics.       |
| `table_profile`  | Returns table profile statistics such as row count, freshness, null rates, distributions, min/max, and quartiles. |
| `lineage`        | Traces lineage at table, column, or Data Product level with upstream, downstream, and impact context.             |

## Design and build

| Tool                     | What it does                                                                                  |
| ------------------------ | --------------------------------------------------------------------------------------------- |
| `design_data_product`    | Guides the design of a new Data Product from a plain-language use case.                       |
| `build_data_product`     | Guides implementation of an approved design spec into a working Data Product project.         |
| `advise_design`          | Generates or refines a design spec with entities, grain, measures, metrics, and dimensions.   |
| `scaffold_generator`     | Generates a project file manifest for seeds, models, semantics, checks, and related files.    |
| `get_component_template` | Returns Vulcan syntax templates and placement guidance for component types.                   |
| `retrieve_examples`      | Fetches working Vulcan code examples by file category and SQL engine.                         |
| `enrich_metadata`        | Adds column descriptions, PII labels, glossary terms, and governance tags into project files. |
| `review_code`            | Reviews Vulcan SQL or YAML for errors and returns corrected code.                             |
| `suggest_quality_checks` | Generates DQ check rules and `MODEL()` assertions for a specific model.                       |
| `explain_concept`        | Explains Vulcan or DataOS concepts in plain language with examples.                           |

## What these tools do not do

These tools keep the assistant on the governed Data Product surface.

If a question cannot be answered by the available semantic surface, Data Product MCP returns a limitation or validation error instead of inventing a metric.


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