MCP server

Précis Finance MCP

io.github.precis-finance/precis-finance-mcp

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

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

Website Source

At a glance

Type

MCP server

Publisher

Not verified

Status

Working at last check

Tools

20

Token cost

About 3,671 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http precis-finance-mcp https://mcp.precis.finance/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.precis-finance-mcp]
url = "https://mcp.precis.finance/mcp"

Gemini CLI

{"mcpServers":{"precis-finance-mcp":{"httpUrl":"https://mcp.precis.finance/mcp"}}}

Cursor

{"mcpServers":{"precis-finance-mcp":{"url":"https://mcp.precis.finance/mcp"}}}

VS Code

{"servers":{"precis-finance-mcp":{"type":"http","url":"https://mcp.precis.finance/mcp"}}}

Antigravity

agy mcp add precis-finance-mcp https://mcp.precis.finance/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"precis-finance-mcp","server_url":"https://mcp.precis.finance/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"precis-finance-mcp","server_url":"https://mcp.precis.finance/mcp"}

Tools

• eval_chart_transform ~210 tokens
  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 ~110 tokens
  Export a cached data_ref to json or xlsx without rerunning the query; returns a temporary file and download link.
• get_binding ~90 tokens
  Fetch one data feed's full configuration: source, target dataset, schedule, and extract parameters.
• get_file_download ~80 tokens
  Renew a download capability for an available, owned artifact.
• get_inspection_schema ~81 tokens
  Get the column schema for an inspection source.
• get_load_status ~91 tokens
  Fetch one data load's full detail by load_id — timestamps, status, rows landed, and any error message.
• inspect_rows ~269 tokens
  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.
• list_bindings ~126 tokens
  List the configured data feeds (ingestion bindings) with their schedule — which datasets load, from where, how often.
• list_dimensions ~94 tokens
  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_inspection_sources ~62 tokens
  List the row-level sources available for inspection.
• list_kpis ~68 tokens
  Browse the metric catalogue — metric keys, formats, domains, and the dimensions available per metric.
• list_load_history ~211 tokens
  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?".
• list_scenarios ~58 tokens
  List the available planning scenarios and their status.
• list_variants ~76 tokens
  List the what-if variants of a scenario.
• precis_orientation ~88 tokens
  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.
• run_metric ~447 tokens
  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 ~425 tokens
  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_statement ~478 tokens
  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 ~456 tokens
  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
• search_hierarchy ~151 tokens
  Search the dimension hierarchies (cost centres, accounts, …) to find valid codes and ids before composing a query.

Browse

• GitHub tools
• Spreadsheets
• Converting to Markdown
• GitHub and code hosting

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