Précis Finance MCP

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

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What it can do

    What data it sees

    Do you need an account

    No: the server works without sign-in

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

    How to connect

    How to connect Précis Finance MCP to Claude

    The server address to paste into Claude settings:

    https://mcp.precis.finance/mcp

    1. Open Claude (claude.ai or the desktop app).
    2. Go to Settings → Connectors.
    3. Click Add custom connector.
    4. Paste the server address copied below into Remote MCP server URL and click Add.
    5. In a chat, click + → Connectors and switch the new connector on.

    Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

    Authorization

    After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

    How to connect Précis Finance MCP to ChatGPT

    The server address to paste into ChatGPT settings:

    https://mcp.precis.finance/mcp

    1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
    2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
    3. Open Settings → Connectors and click Create.
    4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
    5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
    6. In a new chat open + → Apps/Connectors and enable the connector.

    OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

    Authorization

    On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

    How to connect Précis Finance MCP to Cursor

    The server address to paste into Cursor settings:

    https://mcp.precis.finance/mcp

    Fastest: click Open in Cursor below and confirm the prompt.

    Manually:

    1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
    2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
    3. Save the file. The server appears in the MCP list; authorize it there if asked.

    Authorization

    If the service needs sign-in, an authorize button appears next to the server in the MCP list.

    Server tool list (17)

    Raw names from tools/list. Only developers need these.

    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.
    list_scenariosList the available planning scenarios and their status.
    list_kpisBrowse the metric catalogue — metric keys, formats, domains, and the dimensions available per metric.
    list_inspection_sourcesList the row-level sources available for inspection.
    get_inspection_schemaGet the column schema for an inspection source.
    inspect_rowsInspect 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_statementRun 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 %. Shows the user a formatted table.
    run_statement_dataRun 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 the `data_ref` to eval_chart_transform. Does not show the user a table.
    run_metricBreak 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 %. Shows the user a formatted table.
    run_metric_dataBreak 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 the `data_ref` to eval_chart_transform. Does not show the user a table.
    search_hierarchySearch the dimension hierarchies (cost centres, accounts, …) to find valid codes and ids before composing a query.
    list_dimensionsList 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_variantsList the what-if variants of a scenario.
    list_load_historyList 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_statusFetch one data load's full detail by load_id — timestamps, status, rows landed, and any error message.
    list_bindingsList the configured data feeds (ingestion bindings) with their schedule — which datasets load, from where, how often.
    get_bindingFetch one data feed's full configuration: source, target dataset, schedule, and extract parameters.