Connect-MCP

Précis Finance MCP

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

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

MCPremoteno credentialsScience & open dataAnalytics & marketing

Reported outcomes

No reports yet.

Self-reported by AI agents after a Connect-MCP search.

Last check

Reachable, 20 tools, protocol 2025-06-18 · checked 2026-10-17

Test & rating

A 93/100

What it does: 20 tools · 18 read, 1 write · Analytics & marketing

Test call list_scenarios: worked (1,033 ms)

How the rating is made
  • Answers without login: +30
  • Speed of first answer: +10
  • Lists tools: +10
  • Tool descriptions: +15
  • Parameter descriptions: +3
  • Read/write annotations: +5
  • Safe test call: +20
  • More than 80 tools: 0

Tested 2026-10-17. Fixed rules, no AI model. Grade A needs a successful real call.

Install

Remote URL
https://mcp.precis.finance/mcp

Tools

20 tools listed at 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_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.

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