Précis Finance MCP

Create a chart from data_refs (an array of snapshot IDs), a bounded JavaScript function, and chart_spec. Supports line, bar, area, waterfall, scatter, horizontal_bar, forecast_line, cohort_heatmap and sparkline_row. Query first and inspect rows; the transform takes one result per ref and returns flat rows. Cached values are already scaled: scale 3 means non-exempt metrics are in thousands; scale 0 means units. Percentages (75 means 75%), FTEs and other scale-exempt metrics retain their units. Do not scale again; y_scale is forced to 0. For multiple sources, inspect each scale and format; re-query same-unit series at a common scale, and use separately labelled axes for different units (line/bar/area) or separate charts. Conflicting nonempty caption scale labels are omitted; unlabelled sources leave other labels intact. Set axis unit labels explicitly.

Export a previously returned data_ref to json or xlsx without rerunning the query. Use this for standard Excel exports of reports already shown, rather than rebuilding their JSON with Python. Financial XLSX uses shared server formatting and retains source/region metadata for add-in conversion. Returns a temporary file/download link; an expired snapshot requires a fresh authorized query, whose values may change.

Fetch one data feed's full configuration: source, target dataset, schedule, and extract parameters.

Renew a download capability for an available, owned artifact.

Get the column schema for an inspection source.

Fetch one data load's full detail by load_id — timestamps, status, rows landed, and any error message.

Inspect the row-level detail behind a figure, from an enabled inspection source. Returns a capped sample for reasoning plus a grid for the user.

List the configured data feeds (ingestion bindings) with their schedule — which datasets load, from where, how often.

List the dimensions defined in the model — keys, labels, and kinds (leaf / derived / ragged hierarchy). Catalogue metadata only; use search_hierarchy to list a dimension's members.

List the row-level sources available for inspection.

Browse the metric catalogue — metric keys, formats, domains, and the dimensions available per metric.

List data-load attempts from the ingestion audit trail — when each dataset landed, with what status. Answers "is April in yet?" / "when was this data last loaded?".

List the available planning scenarios and their status.

List the what-if variants of a scenario.

Call this first. Returns how to use Précis over this connector: the data model (scenarios, metrics, statements, dimensions), the reporting-tool variants, and how to build charts. Read it before composing queries.

Break one or more metrics down by a dimension — revenue by project, utilisation by employee, headcount trends, GL account drill-down. Rows are the dimension; columns are metrics × scenarios. Pass `scenarios` explicitly and give every scenario a concise, user-facing `alias` such as Actuals, Budget, Variance, or Var %. Returns a formatted table with structured data. The widget already displays this table to the user. Add useful interpretation; do not reproduce the table or restate all its rows in Markdown unless the user explicitly requests it or the widget is unavailable.

Break one or more metrics down by a dimension — revenue by project, utilisation by employee, headcount trends, GL account drill-down. Rows are the dimension; columns are metrics × scenarios. Pass `scenarios` explicitly and give every scenario a concise, user-facing `alias` such as Actuals, Budget, Variance, or Var %. Returns the raw figures (and a `data_ref`) for your own analysis or to build a chart — pass `[data_ref]` as `data_refs` to eval_chart_transform. Does not show the user a table.

Run a financial statement — P&L, variance report, or executive summary. Rows are statement lines (Revenue, Direct Cost, Gross Margin, …); columns are scenarios. Supports an optional dimension breakdown (e.g. by period or cost centre). For an unspecified general P&L, prefer `full_pnl` when it is listed by precis_orientation. Give every scenario a concise, user-facing `alias` such as Actuals, Budget, Variance, or Var %. Returns a formatted table with structured data. The widget already displays this table to the user. Add useful interpretation; do not reproduce the table or restate all its rows in Markdown unless the user explicitly requests it or the widget is unavailable.

Run a financial statement — P&L, variance report, or executive summary. Rows are statement lines (Revenue, Direct Cost, Gross Margin, …); columns are scenarios. Supports an optional dimension breakdown (e.g. by period or cost centre). For an unspecified general P&L, prefer `full_pnl` when it is listed by precis_orientation. Give every scenario a concise, user-facing `alias` such as Actuals, Budget, Variance, or Var %. Returns the raw figures (and a `data_ref`) for your own analysis or to build a chart — pass `[data_ref]` as `data_refs` to eval_chart_transform. Does not show the user a table.

Search the dimension hierarchies (cost centres, accounts, …) to find valid codes and ids before composing a query.