Précis Finance MCP

Last 30 days

Uptime

100%

Response time

194 ms typical, 194 ms slowest 5%

Last check

3 h ago

Next check

in 2 h

How to call it

Add it to any MCP client that supports remote servers.

{
  "mcpServers": {
    "precis-finance-mcp": {
      "type": "http",
      "url": "https://mcp.precis.finance/mcp"
    }
  }
}

20 tools

• 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_

• 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_

• 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 a

• 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 a

• 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.

• eval_chart_transform

  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

• export_dataset

  Export a cached data_ref to json or xlsx without rerunning the query; returns a temporary file and download link.

• get_file_download

  Renew a download capability for an available, owned artifact.

• 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.

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