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Précis Finance MCP

io.github.precis-finance/precis-finance-mcp · v0.4.0

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

Connect Remote server

Terminal
claude mcp add --transport http precis-finance-mcp https://mcp.precis.finance/mcp
~/.cursor/mcp.json
{
  "mcpServers": {
    "precis-finance-mcp": {
      "url": "https://mcp.precis.finance/mcp"
    }
  }
}
.vscode/mcp.json
{
  "servers": {
    "precis-finance-mcp": {
      "type": "http",
      "url": "https://mcp.precis.finance/mcp"
    }
  }
}
Claude Desktop
Settings > Connectors > Add custom connector
URL: https://mcp.precis.finance/mcp

Tools 20 found in our last check

  • 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…

  • 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…

  • 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 pe…

  • export_dataset

    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…

  • 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.

Details

Endpointhttps://mcp.precis.finance/mcp (Streamable HTTP)
First published5 Nov 2026
Last updated5 Nov 2026
Registry nameio.github.precis-finance/precis-finance-mcp

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