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Précis Finance MCP

io.github.precis-finance/precis-finance-mcp · v0.3.0

Measured by MCP Station

Description written by the publisher, not verified

Public read-only Précis Finance MCP demo with synthetic data; no account or credentials required.

Connect

claude mcp add --transport http precis-finance-mcp https://mcp.precis.finance/mcp

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

Tools as reported by the server

• 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 list
• 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 list
• 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, V
• 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, V
• 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 ref and returns fla
• 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.

Repository · Website · JSON · Text for agents · Registry updated 2026-10-09