# Précis Finance Muse connector

From musedirectory.ai, the independent directory of Meta Muse connectors. Not affiliated with Meta.

## Précis Finance

Analyze financial statements, metrics and planning scenarios with drill-down and variance reporting.

- Record: https://musedirectory.ai/connector/pre-cis-finance
- Category: Finance & Bills
- Developer: Précis Finance (https://precis.finance/mcp)
- Muse status: Extra setup. Not in Muse's Connectors list yet. Muse can still use it: its page gives you a request to paste into Muse.
- Health: Working, 379ms, checked 2026-09-28T07:30:50Z
- Endpoint: https://mcp.precis.finance/mcp
- Auth: No account needed; Pricing: unknown
- Screening: Screened, no issues found (2026-09-25T19:11:34Z)
- Source: Found in the official MCP Registry (io.github.precis-finance/precis-finance-mcp) https://registry.modelcontextprotocol.io/v0/servers?search=io.github.precis-finance%2Fprecis-finance-mcp

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.

Example request: "Show me revenue by project for this month compared to budget, with variance."

How to connect: Not 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.

Tools:
- 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_
- 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_
- 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 a
- 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 a
- 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.
- 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.

Screening checks:
- MCP handshake: pass (Answered in 762ms)
- Domain against threat feeds (Cloudflare security DNS): pass (mcp.precis.finance, precis.finance not flagged)
- Published packages against the OSV malicious-package database: n/a (No npm or PyPI package published)
- Hidden instructions or invisible characters in tool text: pass (17 tools read, nothing found)
- Inputs asking for passwords, card numbers or seed phrases: pass (None found)
- Domain and redirects: pass (No redirects off the domain)
- AI review of purpose and tool behavior: pass (No concerns)