Public read-only Précis Finance MCP demo with synthetic data; no account or credentials required.
io.github.precis-finance/precis-finance-mcp MCP Server
A public read-only Model Context Protocol (MCP) demo for Précis Finance focused on FP&A and management reporting. It uses synthetic data and does not require accounts or credentials. The server is self-hosted and exposed as an MCP server with a documented interface for financial analysis and reporting.
🛠️ Key Features
Public read-only MCP demo
Synthetic data (no accounts or credentials required)
Self-hosted MCP server
Topics include governed metrics, semantic-layer, and financial reporting
🚀 Use Cases
FP&A workflows
Management reporting
Financial analysis and financial reporting
Driven by governed metrics / management-reporting focus
⚡ Developer Benefits
No authentication setup needed
Fits MCP-based Model Context Protocol integrations
Provides a semantic-layer and governed-metrics-oriented approach for FP&A and reporting
⚠️ Limitations
Read-only; does not support writing or account-based operations
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.
Parameters
No parameters.
Raw schema
{
"type": "object",
"properties": {}
}
list_scenarios
List the available planning scenarios and their status.
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 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 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.
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, Variance, or Var %. Shows the user a formatted table.
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, 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.
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 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?".
A read-only, self-hosted MCP server for FP&A
and management reporting: governed metrics, financial statements, and row-level
drill-down over a SQL semantic layer — your own warehouse, not market data.
The open core of Précis, the agentic 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. The semantic views are plain SQL in your ClickHouse, so your
existing BI tools can query the same views directly. This repository is complete and
self-hostable — the metric engine, semantic layer and catalogue, ingestion,
identity, and the MCP transport — and depends on nothing outside it.
Use this if
You run or support a finance function and want AI clients — Claude, ChatGPT,
or any MCP-capable client — answering questions and pulling management
reporting from your own numbers.
You need governed metrics — one agreed definition of gross margin,
utilisation, or revenue by business unit — not an agent querying raw rows in
the general ledger or inventing its own definitions.
You want it self-hosted, on infrastructure you already operate and under a
security model you control — not another cloud contract.
You need every answer traceable: read-only by construction, no figure
generated by the model, every number aggregated from semantic views you can
read.
Requirements
Docker — Engine + Compose v2. Everything runs in containers; there is
nothing else to install.
No warehouse to provision — the stack bundles ClickHouse (the engine's
analytical store) and PostgreSQL (platform state — users, sessions, audit). A
local trial runs with nothing else and loads a sample finance model.
Already run ClickHouse? — optionally point the engine at your own instance
instead of the bundled one; see
data modes.
An MCP-capable client — Claude (claude.ai and Desktop connectors, against
a deployed instance), Claude Code, ChatGPT, or any MCP client.
Optional Excel access — the multi-user image also hosts the Précis Excel
add-in at /excel; enable its OAuth client when you want live read-only
workbook functions.
What Précis Finance MCP looks like
Claude, connected to Précis over MCP, rendering a governed P&L with comparatives and then drilling Revenue down to cost-centre level — sample data
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.
Try the public demo
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:
sh
claude mcp add --transport http precis-demo https://mcp.precis.finance/mcp
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.
Quickstart — single-user local trial
sh
export MCP_DEV_KEY=$(openssl rand -hex 32)
docker compose -f deploy/docker-compose.local.yml up -d --build
docker compose -f deploy/docker-compose.local.yml exec precis-mcp \
python -m precis_mcp.sample_data # populate the demo model
One stack — the server plus a bundled ClickHouse and Postgres — 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."
Rather do this on a call? We pair with early adopters to stand Précis Finance MCP up
against their own warehouse — precis.finance or
hello@precis.finance.
Multi-user deployment
deploy/docker-compose.yml runs the multi-user stack — the open server,
PostgreSQL, ClickHouse, and OAuth 2.1 + PKCE sign-in. Deployment is modular
along independent axes:
Ingress — the bundled Caddy proxy (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)
Backups — additive 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 add-in — optional hosted workbook functions served from the same
instance at /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.
Read-only by construction — the server retrieves; it never writes to or
changes your source. There is no write path back.
No figure is generated by the model — tools return numbers aggregated from
source dimensions (account, cost centre, period, scenario), or report that the
data isn't there. The client composes the question and narrates the answer; it
never invents a figure.
Traceable to source — every number traces from response → semantic view →
warehouse. The semantic views are plain SQL you can read; the catalogue is
versioned in git.
Your own warehouse, not market data — it runs in your environment, against
your warehouse, under your own access controls.
Identity — a local dev key, the bundled Keycloak (optionally federated to
your IdP), or a direct external OIDC provider (Auth0 / Okta / Entra / Ping).
Operator responsibility — Précis Finance MCP is designed to expose governed read
operations; you still deploy and operate it under your own security model.
Disclosure policy: SECURITY.md.
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.
What ships — and what doesn't
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).
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 hello@precis.finance. The same address reaches
the team behind the full Précis platform.
Contributing
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.
License
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.
