io.github.precis-financePublished by GitHub account precis-financev0.4.0Updated Nov 5, 2026
Public read-only Précis Finance MCP demo with synthetic data; no account or credentials required.
The publisher's own server: Finance Foundation
Read-only FP&A connector that lets an assistant query governed financial statements, metric breakdowns, row-level detail, charts and Excel exports.
Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.
Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.
Read https://sourceweft.com/mcp/io.github.precis-finance%2Fprecis-finance-mcp/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.Add this to your client's mcpServers config.
{
"mcpServers": {
"pr-cis-finance": {
"type": "http",
"url": "https://mcp.precis.finance/mcp"
}
}
}[License: Elastic 2.0] [MCP] [CI] [Docs]
A read-only, self-hosted Model Context Protocol (MCP) server for financial reporting on Databricks or ClickHouse, built for Financial Planning & Analysis (FP&A) and management reporting. Give Claude, ChatGPT or another MCP-capable client access to governed metrics, financial statements, variance comparisons, row-level drill-down, charts and Excel exports from your own financial data. It is source-available under Elastic License 2.0 and forms the open core of Précis, the Finance Intelligence Platform.
You describe your business metrics once — KPIs, hierarchies, and financial-statement layouts in a YAML catalogue over a semantic layer of plain SQL views, versioned in git — and any MCP-capable client gets consistent, defensible numbers. Use your existing Databricks data model or start a local self-contained service with the bundled ClickHouse engine. Your own financials, not market data.
/excel; enable its OAuth client when you want live read-only
workbook functions.Every figure aggregates from the semantic views — account, cost centre, period, scenario — and the measures generic metric layers can't express (a utilisation denominator averaged across periods, re-aggregated by quarter or business unit, with subtotals in the layout) are first-class catalogue definitions.
Connect a remote MCP client to https://mcp.precis.finance/mcp to explore Précis Finance MCP without installing anything or creating an account. The shared server exposes read-only tools over a synthetic finance dataset; it does not contain your financial data. No API key or sign-in is required.
For Claude Code:
Then ask precis_orientation what the demo supports, or request a P&L with
comparatives. See the public demo connection guide
for other MCP clients and the limits of this shared instance.
One stack — the server plus ClickHouse, Postgres and Valkey — a shared dev
key, bound to 127.0.0.1. Point any MCP client at http://127.0.0.1:8768/mcp
with the dev key as bearer token. For example, with Claude Code:
Then ask a finance-specific question to prove this is not a generic SQL MCP: "Show the P&L for FY2025 with comparatives." · "Drill revenue down by cost centre." · "Show utilisation by month for the Digital Transformation team."
Full walkthrough, including the demo data and bringing your own model: docs/getting-started/quickstart.md.
Rather do this on a call? We pair with early adopters to stand Précis Finance MCP up against their own warehouse — precis.finance or [email protected].
deploy/docker-compose.yml runs the multi-user stack — the open server,
PostgreSQL, ClickHouse (if using the bundled deployment), Valkey, artifact
storage, and OAuth 2.1 + PKCE sign-in. Deployment is modular along independent axes:
bundled-proxy profile, on by
default) terminates TLS with automatic Let's Encrypt certificates from
nothing but PRECIS_DOMAIN. The app itself is proxy-agnostic: drop the
profile to front it with an ingress you already operate (reference nginx
vhost in deploy/nginx/, host helper scripts/install-precis-mcp.sh --nginx)backup compose profile: scheduled bundles
(Postgres dump + ClickHouse backup + instance config) to a local volume or
S3, with restore and drill commands:
docs/operations/backups.md/excel; enable with the bundled Keycloak client or a public
client in your external IdP:
docs/excel/scripts/deploy-mcp.sh --data-mode ... --auth-mode ... is the friendly front
door over the Compose profiles. Every knob is an environment variable:
docs/configuration/environment-variables.md.
Your model lives in an instance/ directory — metric catalogue, semantic SQL
views, ingestion bindings — and the bundled demo instance shows the shape.
Start at docs/configuration/catalogue-and-semantic.md,
then docs/configuration/ingestion.md and
docs/operations/onboarding-ingestion.md
to load your data. For Databricks, see the
Databricks schema contract.
The developer guide for adding server tools is
docs/development/adding-read-tools.md.
Start with the mechanisms, not the promise.
Use your existing Databricks environment as the analytical backend for Précis Finance MCP. Keep your financial data and pipelines in place, without copying the inputs into another analytical database.
Map your data to reporting views and agree the metric and statement definitions; finance can then request a monthly P&L, compare actuals with budget or drill into the drivers of a variance through a compatible MCP client.
What Précis adds alongside Databricks Genie: consistent financial calculations and presentation. Your agreed aggregation rules, statement layouts, comparison columns and number formats carry through each report. The agent requests established financial operations from the reporting engine, reducing repeated query generation and the scope for calculation errors as the investigation develops.
Our FP&A case study: Précis and Databricks Genie compares five financial requests against the same Databricks data, showing the differences in calculation fidelity, financial presentation and waiting time. Précis completed responses in 16–35 seconds, compared with 73–108 seconds for Genie in that session. The study compares the full Précis workspace with the native Genie interface; Finance MCP uses the same reporting engine, while response times depend on the connected client and deployment.
Read the Databricks product update for the broader platform benefits, including approved planning writeback. Précis Finance MCP remains read-only.
Full documentation: docs.precis.finance.
The deployment and data model you build here are the foundation the full Précis platform runs on. Précis is currently pre-General Availability and adds a licensed agentic workspace over this same engine and data model. Moving from the open core to Précis is workflow configuration and adoption — not a second data-integration project. Précis prepares; the finance professional decides.
Ships in Précis Finance MCP (Elastic License 2.0): the MCP server and transport · metric engine · financial-statement layouts · semantic SQL view pattern · YAML metric catalogue · sample finance model · ingestion path · hosted read-only Excel add-in · ClickHouse analytical store · PostgreSQL platform state · local dev-key mode · multi-user OAuth 2.1 mode · Docker Compose deployment · backup & restore profile · configuration and deployment docs.
Lives in Précis, not in this repo: the workspace UI · the conversational agent · plan write-back · scenario commit workflows · scheduled Dispatch briefings · report / management-pack workflow · Excel write-back / round-trip · commercial support (unless separately agreed).
Talk about Précis — the full platform and design-partner programme: precis.finance or [email protected].
Précis Finance MCP is built to be self-serve, and the documentation aims to be enough. If you'd like help beyond it — configuring your data model, a guided deployment, ongoing support, or a demo environment to evaluate against realistic data — book a setup session at precis.finance or write to [email protected]. The same address reaches the team behind the full Précis platform.
This repository is a one-way mirror of the Précis monorepo: main advances
by sync commits, and pull requests are applied internally with your authorship
and DCO sign-off preserved, then published in the next sync. See
CONTRIBUTING.md.
Elastic License 2.0 — source-available. Free to use, modify, self-host (including commercially), and redistribute; you may not offer it to third parties as a hosted or managed service.
Source: README.md at commit f536a5c
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.
Takes no parameters.
List the available planning scenarios and their status.
Takes no parameters.
Browse the metric catalogue — metric keys, formats, domains, and the dimensions available per metric.
Takes no parameters.
List the row-level sources available for inspection.
Takes no parameters.
Get the column schema for an inspection source.
source_keyrequiredInspect the row-level detail behind a figure, from an enabled inspection source. Returns a capped sample for reasoning plus a grid for the user.
limitcolumnsfiltersperiod_endsource_keyrequiredscenario_idrequiredperiod_startRun 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 a formatted table with structured data. The widget already displays this table to the user. Add useful interpretation; do not reproduce the table or restate all its rows in Markdown unless the user explicitly requests it or the widget is unavailable.
scalefiltersdecimalsscenariosstatementdimensionsperiod_endperiod_startRun 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 `[data_ref]` as `data_refs` to eval_chart_transform. Does not show the user a table.
scalefiltersdecimalsscenariosstatementdimensionsperiod_endperiod_startBreak 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 a formatted table with structured data. The widget already displays this table to the user. Add useful interpretation; do not reproduce the table or restate all its rows in Markdown unless the user explicitly requests it or the widget is unavailable.
scalefiltersmetricsrequireddecimalsscenariosdimensionsperiod_endperiod_startBreak 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 `[data_ref]` as `data_refs` to eval_chart_transform. Does not show the user a table.
scalefiltersmetricsrequireddecimalsscenariosdimensionsperiod_endperiod_startSearch the dimension hierarchies (cost centres, accounts, …) to find valid codes and ids before composing a query.
limitquerydimensionList 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.
Takes no parameters.
List the what-if variants of a scenario.
scenario_idrequiredCreate 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 flat rows. Cached values are already scaled: scale 3 means non-exempt metrics are in thousands; scale 0 means units. Percentages (75 means 75%), FTEs and other scale-exempt metrics retain their units. Do not scale again; y_scale is forced to 0. For multiple sources, inspect each scale and format; re-query same-unit series at a common scale, and use separately labelled axes for different units (line/bar/area) or separate charts. Conflicting nonempty caption scale labels are omitted; unlabelled sources leave other labels intact. Set axis unit labels explicitly.
data_refsrequiredchart_specrequiredtransform_coderequiredExport 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-in conversion. Returns a temporary file/download link; an expired snapshot requires a fresh authorized query, whose values may change.
formatrequireddata_refrequiredRenew a download capability for an available, owned artifact.
file_idrequiredList 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?".
limitperiodstatusbinding_iddataset_idFetch one data load's full detail by load_id — timestamps, status, rows landed, and any error message.
load_idrequiredList the configured data feeds (ingestion bindings) with their schedule — which datasets load, from where, how often.
targetsource_idFetch one data feed's full configuration: source, target dataset, schedule, and extract parameters.
binding_idrequired90% of the applicable checks met
How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.
Declare a license in the repository or the registry entry.
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claude mcp add --transport http precis-demo https://mcp.precis.finance/mcpexport MCP_DEV_KEY=$(openssl rand -hex 32)export ARTIFACT_SIGNING_KEY=$(openssl rand -hex 32)docker compose -f deploy/docker-compose.local.yml up -d --builddocker compose -f deploy/docker-compose.local.yml exec precis-mcp \ python -m precis_mcp.sample_data # populate the demo modelclaude mcp add --transport http precis http://127.0.0.1:8768/mcp \ --header "Authorization: Bearer $MCP_DEV_KEY"