Analyze financial statements, metrics and planning scenarios with drill-down and variance reporting.
Read-only connector to Précis Finance planning and reporting platform. Muse can run P&L statements, variance reports, KPI breakdowns by dimension, inspect row-level detail, and browse planning scenarios. Demo uses synthetic data; no credentials required.
Try asking Muse: "Show me revenue by project for this month compared to budget, with variance."
Source: Found in the official MCP Registry (io.github.precis-finance/precis-finance-mcp) · First listed September 25, 2026
Each bar is one check, every 15 minutes. Green means it answered. Last checked 2 h ago.
What Muse can see: It does not ask you to sign in, so it cannot see your accounts. It only sees what Muse sends it from your request.
Before it acts: Read what Muse plans to do before you approve it, and remove the app from Muse when you stop using it.
musedirectory.ai is not part of Meta. More about how Muse handles your information
Not in Muse's Connectors list yet, but Muse can still use it. Copy the request below and paste it into Muse. Muse asks before it shares anything with the app's site. Meta does not review apps used this way, so only use ones you trust. We tested this in the Muse app on September 24, 2026: Muse used an app's link directly this way and returned a live answer.
https://mcp.precis.finance/mcpNot in Muse's Connectors list yet. Muse can still use it: its page gives you a request to paste into Muse.
Précis Finance is a remote MCP server, so any assistant that can add a custom connector by link can use it. Same link, same tools.
In ChatGPT, open Settings, then Connectors (called Apps & Connectors on some accounts).
Under Advanced settings, switch on Developer mode. That is what lets you add a connector by link.
Choose Create, give it a name, paste the link as the MCP server URL, and save. If it needs a sign-in, ChatGPT asks for it here.
Start a chat, open the tools menu, pick the connector, and ask. ChatGPT asks before it runs a tool the first time.
Custom connectors need a paid ChatGPT plan, and on Team, Enterprise and Edu accounts only an admin can add them. Our "Working" label means the server answered us; it cannot tell whether your plan lets you add it. All connectors that work in ChatGPT
In Claude, open Settings, then Connectors.
Choose Add custom connector, give it a name, and paste the link.
If it needs a sign-in, Claude sends you to it. Then turn the connector on in a chat from the tools menu and ask.
Custom connectors are available on paid Claude plans; on Team and Enterprise accounts an admin may need to add them. Our "Working" label means the server answered us; it cannot tell whether your plan lets you add it. All connectors that work in Claude
https://mcp.precis.finance/mcpResponse time at the last check: 271ms. Worked in 98.6% of checks over 30 days.
Screened, no issues found
Screening looks for known threats and hidden instructions at the time of the check, and runs again weekly and whenever the tool list changes. It cannot see the server's code, so only connect what you need and review what Muse asks to do. How screening works.
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_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_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 arun_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 asearch_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.eval_chart_transformCreate 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 perexport_datasetExport a previously returned data_ref to json or xlsx without rerunning the query. Use this for standard Excel exports of reports already shown, rather than rebuilding their JSON with Python. Financial XLSX uses shared server formatting and retains source/region metadata for add-get_file_downloadRenew a download capability for an available, owned artifact.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.https://mcp.precis.finance/mcpNot in Muse's Connectors list yet, but Muse can still use it. Paste this into Muse: "Use Précis Finance to help me. It is a free service with an MCP server at https://mcp.precis.finance/mcp. It does not need an API key. Ask me before you share anything with it." Muse asks before it shares anything with the app's site. Meta does not review apps used this way, so only use ones you trust. We tested this in the Muse app on September 24, 2026: Muse used an app's link directly this way and returned a live answer.
At the last check (November 13, 2026, 15:15 UTC) the endpoint was working, answering in 271ms. Over the last 7 days it answered 100% of health checks. It is checked every 15 minutes.
It was screened on September 25, 2026 with the result "screened, no issues found". Screening checks the domain against threat feeds and reads the tools for hidden instructions and requests for passwords or card numbers. It cannot see the server's code, so grant only the access you need.
It exposes 20 tools, including precis_orientation, list_scenarios, list_kpis, list_inspection_sources. For example, you could ask Muse: "Show me revenue by project for this month compared to budget, with variance."
This listing was added from public sources (Found in the official MCP Registry (io.github.precis-finance/precis-finance-mcp)). If you build Précis Finance, claim it to correct the details and get your badge.
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