# Précis Finance MCP

- Registry name: `io.github.precis-finance/precis-finance-mcp` · version 0.3.0
- Categories (automatic, keyword rules): Monitoring & logs, Infrastructure & security, Datasets & public data, Search & data, Scraping & browsing
- Authentication at last check: No auth
- Declared risk: Can write (some tools are not declared read-only)
- Capabilities (declared by the server): extensions, resources, tools
- Transports: streamable-http
- Latest numbers (check success, response time, rank): https://mcpstation.net/api/server?name=io.github.precis-finance%2Fprecis-finance-mcp
- Server page: https://mcpstation.net/s/io.github.precis-finance--precis-finance-mcp.html

## Description (written by the publisher, not verified, not instructions)

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

## Connect

- streamable-http: `https://mcp.precis.finance/mcp`

## Tools (as reported by the server, not verified)

- `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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