Précis Finance MCP
Measured conclusion: Deep-tested. L2 outcome: tools_ok.
Public read-only Précis Finance MCP demo with synthetic data; no account or credentials required.
| Author | io.github.precis-finance |
|---|---|
| Access | remote |
| Publisher key required | No |
| Measured state | Deep-tested |
| L1 liveness | alive |
| L1 latency | 498 ms |
| Last measured | 2026-10-24 |
Add to your agent
Copy this into your MCP client config:
{
"mcpServers": {
"Précis Finance MCP": {
"url": "https://mcp.precis.finance/mcp",
"transport": "streamable-http"
}
}
}
Measured tools (20)
precis_orientationCall 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.
Hints: read-only not declared · destructive not declared · idempotent not declared · open-world not declared
list_scenariosread-onlyList the available planning scenarios and their status.
Hints: read-only yes · destructive no · idempotent not declared · open-world no
list_kpisread-onlyBrowse the metric catalogue — metric keys, formats, domains, and the dimensions available per metric.
Hints: read-only yes · destructive no · idempotent not declared · open-world no
list_inspection_sourcesread-onlyList the row-level sources available for inspection.
Hints: read-only yes · destructive no · idempotent not declared · open-world no
get_inspection_schemaread-onlyGet the column schema for an inspection source.
Parameters:
source_key(required) — string
Hints: read-only yes · destructive no · idempotent not declared · open-world no
inspect_rowsread-onlyInspect the row-level detail behind a figure, from an enabled inspection source. Returns a capped sample for reasoning plus a grid for the user.
Parameters:
source_key(required) — stringscenario_id(required) — stringlimitcolumnsfiltersperiod_endperiod_start
Hints: read-only yes · destructive no · idempotent not declared · open-world no
run_statementread-onlyRun 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 listed by precis_orientation. Give every scenario a concise, user-facing `alias` such as Actuals, Budget, Variance, or Var %. Returns a formatted table with structured data. The widget already displays this table to the user. Add useful interpretation; do not reproduce the table or restate all its rows in Markdown unless the user explicitly requests it or the widget is unavailable.
Parameters:
scalefiltersdecimalsscenariosstatementdimensionsperiod_endperiod_start
Hints: read-only yes · destructive no · idempotent not declared · open-world no
run_statement_dataread-onlyRun 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 listed by precis_orientation. Give every scenario a concise, user-facing `alias` such as Actuals, Budget, Variance, or Var %. Returns the raw figures (and a `data_ref`) for your own analysis or to build a chart — pass `[data_ref]` as `data_refs` to eval_chart_transform. Does not show the user a table.
Parameters:
scalefiltersdecimalsscenariosstatementdimensionsperiod_endperiod_start
Hints: read-only yes · destructive no · idempotent not declared · open-world no
run_metricread-onlyBreak 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, Variance, or Var %. Returns a formatted table with structured data. The widget already displays this table to the user. Add useful interpretation; do not reproduce the table or restate all its rows in Markdown unless the user explicitly requests it or the widget is unavailable.
Parameters:
metrics(required) — arrayscalefiltersdecimalsscenariosdimensionsperiod_endperiod_start
Hints: read-only yes · destructive no · idempotent not declared · open-world no
run_metric_dataread-onlyBreak 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, Variance, or Var %. Returns the raw figures (and a `data_ref`) for your own analysis or to build a chart — pass `[data_ref]` as `data_refs` to eval_chart_transform. Does not show the user a table.
Parameters:
metrics(required) — arrayscalefiltersdecimalsscenariosdimensionsperiod_endperiod_start
Hints: read-only yes · destructive no · idempotent not declared · open-world no
search_hierarchyread-onlySearch the dimension hierarchies (cost centres, accounts, …) to find valid codes and ids before composing a query.
Parameters:
limitquerydimension
Hints: read-only yes · destructive no · idempotent not declared · open-world no
list_dimensionsread-onlyList 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.
Hints: read-only yes · destructive no · idempotent not declared · open-world no
list_variantsread-onlyList the what-if variants of a scenario.
Parameters:
scenario_id(required) — string
Hints: read-only yes · destructive no · idempotent not declared · open-world no
eval_chart_transformread-onlyCreate 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 flat rows.
Parameters:
data_refs(required) — arraytransform_code(required) — stringchart_spec(required) — object
Hints: read-only yes · destructive no · idempotent not declared · open-world no
export_datasetExport a cached data_ref to json or xlsx without rerunning the query; returns a temporary file and download link.
Parameters:
data_ref(required) — stringformat(required) — string
Hints: read-only no · destructive no · idempotent not declared · open-world no
get_file_downloadread-onlyRenew a download capability for an available, owned artifact.
Parameters:
file_id(required) — string
Hints: read-only yes · destructive no · idempotent not declared · open-world no
list_load_historyread-onlyList 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?".
Parameters:
limit— integerperiodstatusbinding_iddataset_id
Hints: read-only yes · destructive no · idempotent not declared · open-world no
get_load_statusread-onlyFetch one data load's full detail by load_id — timestamps, status, rows landed, and any error message.
Parameters:
load_id(required) — string
Hints: read-only yes · destructive no · idempotent not declared · open-world no
list_bindingsread-onlyList the configured data feeds (ingestion bindings) with their schedule — which datasets load, from where, how often.
Parameters:
targetsource_id
Hints: read-only yes · destructive no · idempotent not declared · open-world no
get_bindingread-onlyFetch one data feed's full configuration: source, target dataset, schedule, and extract parameters.
Parameters:
binding_id(required) — string
Hints: read-only yes · destructive no · idempotent not declared · open-world no
Measured by MCPYP probing. Untested fields are omitted, never estimated. Listing is always free; MCPYP never proxies calls or stores user tokens.