Lightdash Analytics

Lightdash Analytics

Use the Lightdash MCP server as the source of truth for the user's governed semantic layer.

Discover before querying

1. Set the active project if the server requires one.
2. Use route_agent when available; otherwise list the available explores.
3. Inspect the selected explore and fields before composing a metric query.
4. Use verified content as a reference when it matches the request.

Never invent explore names, field IDs, metric definitions, filter values, or query UUIDs. Search field values when a string filter must match an existing value.

Answering questions

Prefer run_metric_query for questions that fit the semantic layer. Use run_sql, when it is available, only when the requested analysis cannot be represented through a governed explore.

If a metric query is still running, poll get_query_result with the returned query UUID. When a visual would clarify the result, render a completed metric query with render_chart.

State the metric, time period, filters, and any important caveats in the answer. Treat an empty result as a valid result rather than a failed query.

Creating content and changing analytics

Before creating a chart or dashboard, inspect the relevant schema and existing content. Validate the query first, then use the server's creation workflow.

For dbt or content-as-code changes, work in a branch and use the Lightdash CLI workflow: preview the change, validate it, review it, then merge. Do not deploy or start AI writeback unless the user explicitly asks for that external change.

Built-in Lightdash skills

The MCP server can expose additional Lightdash skills and references. Use list_skills when the client does not surface MCP resources directly, then read only the skill or reference relevant to the task.