Précis Finance MCP
Public read-only Précis Finance MCP demo with synthetic data; no account or credentials required.
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What it can do
What data it sees
Do you need an account
No: the server works without sign-in
Public read-only Précis Finance MCP demo with synthetic data; no account or credentials required.
Server tool list (17)
Raw names from tools/list. Only developers need these.
| precis_orientation | 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. |
| list_scenarios | List the available planning scenarios and their status. |
| list_kpis | Browse the metric catalogue — metric keys, formats, domains, and the dimensions available per metric. |
| list_inspection_sources | List the row-level sources available for inspection. |
| get_inspection_schema | Get the column schema for an inspection source. |
| inspect_rows | 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. |
| run_statement | 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 %. Shows the user a formatted table. |
| run_statement_data | 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 the `data_ref` to eval_chart_transform. Does not show the user a table. |
| run_metric | 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 %. Shows the user a formatted table. |
| run_metric_data | 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 the `data_ref` to eval_chart_transform. Does not show the user a table. |
| search_hierarchy | Search the dimension hierarchies (cost centres, accounts, …) to find valid codes and ids before composing a query. |
| list_dimensions | 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_variants | List the what-if variants of a scenario. |
| list_load_history | 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?". |
| get_load_status | Fetch one data load's full detail by load_id — timestamps, status, rows landed, and any error message. |
| list_bindings | List the configured data feeds (ingestion bindings) with their schedule — which datasets load, from where, how often. |
| get_binding | Fetch one data feed's full configuration: source, target dataset, schedule, and extract parameters. |