Use Cursor as the AI agent for your DataUnmess workspace.

Create a DataUnmess MCP key, choose Cursor, copy the generated config or setup prompt, replace the token placeholder locally, and restart Cursor.

Ask Cursor to build an operational dashboard from a source, materialize a cleaned dataset, or document a pipeline with the source and refresh path.

Cursor can reason about application code, schemas, and integration behavior while DataUnmess stores the resulting dashboards, datasets, pipelines, and business definitions. The MCP boundary gives the coding agent explicit tools instead of asking it to hand-code a temporary visualization.

A good first task names the business question and source, asks Cursor to inspect fields, and requires a saved source-backed result. If the source is not analysis-ready, create and validate a pipeline before building the dashboard.

Keep credentials out of prompts. Cursor calls the DataUnmess MCP server with the configured workspace key, while underlying source credentials remain in their DataUnmess connection flow.

Include an acceptance check in the prompt, such as the expected date range, row grain, KPI formula, refresh path, or a known total. Cursor can use that constraint to validate the artifact instead of stopping after the first plausible chart.

When the check passes, save the result with a clear name and description so another workspace member can understand and reuse it without reopening the original coding conversation.

• Create operational dashboards while working on the related application or integration.
• Turn CSV, spreadsheet, API, or database cleanup into a repeatable pipeline.
• Document the metric definition and source path with the saved artifact.
• Return later to update charts or pipeline steps instead of starting over.