io.github.precis-finance/precis-finance-mcp · v0.3.0 · finance-and-payments
Précis Finance MCP is healthy: it completed an MCP handshake in 1069 ms and exposes 20 tools.
Public read-only Précis Finance MCP demo with synthetic data; no account or credentials required.
Endpoint: https://mcp.precis.finance/mcp (streamable-http)
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| Users (last 30 days) | 0 |
| Handshake | Healthy |
| Auth needed | None |
| Tool count | 20 |
| Latency | 1069 ms |
| Last checked (UTC) | 2026-10-16T13:20:40.847Z |
| Checked (UTC) | Status | Latency | Error |
|---|---|---|---|
| 2026-10-16T13:20:40.847Z | Healthy | 1069 | |
| 2026-10-15T08:10:14.334Z | Healthy | 1244 |
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. |