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Précis Finance MCP

Précis Finance MCP

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precis.financeFinance7Updated 3w ago

About Précis Finance MCP

Précis Finance MCP is a self-hosted, read-only server for finance teams querying governed metrics and management reports from their own warehouse. Its SQL semantic layer and YAML catalogue keep metric definitions consistent and traceable.

Key features

  1. 01Governed metrics and statement layouts
  2. 02Row-level drill-down and charts
  3. 03SQL semantic layer and YAML catalogue
  4. 04Read-only, traceable figures
  5. 05Docker-based self-hosted deployment

Use cases

  1. 01Retrieve a P&L with comparatives
  2. 02Drill revenue down by cost centre
  3. 03Review utilisation by month or team
  4. 04Serve governed finance data to MCP clients

MCP support

It exposes read-only finance tools to MCP clients over its own data model and warehouse. The public demo is a remote MCP server with synthetic data and requires no account or API key.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://mcp.precis.finance/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Tools

20

This tool takes no parameters.

Details

From the project README

Précis Finance MCP

License: Elastic 2.0 MCP CI Docs

A read-only, self-hosted MCP server for FP&A and management reporting: governed metrics, financial statements, row-level drill-down, charts and snapshot file exports 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), PostgreSQL (platform and artifact state), and Valkey (temporary snapshots). 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:

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

export 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 --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 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:

claude mcp add --transport http precis http://127.0.0.1:8768/mcp \
  --header "Authorization: Bearer $MCP_DEV_KEY"

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].

Multi-user deployment

deploy/docker-compose.yml runs the multi-user stack — the open server, PostgreSQL, ClickHouse, Valkey, artifact storage, and OAuth 2.1 + PKCE sign-in. Deployment is modular along independent axes:

  • Data — bundled ClickHouse (empty or with sample data) or your own: docs/deployment/clickhouse-data-modes.md
  • Identity — local dev key, the bundled Keycloak (optionally federated to your IdP), or a direct external OIDC provider (Auth0 / Okta / Entra / Ping): docs/deployment/oauth-keycloak.md, docs/deployment/external-idp-recipes.md
  • 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.

Configure your data model

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. The developer guide for adding server tools is docs/development/adding-read-tools.md.

Security and read-only posture

Start with the mechanisms, not the promise.

  • 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.

Documentation

Full documentation: docs.precis.finance.

AreaStart here
Getting startedquickstart
DeploymentClickHouse data modes · OAuth / Keycloak · external IdP recipes
ExcelPrécis for Excel · function reference
Configurationcatalogue & semantic layer · ingestion · environment variables
Operationsonboarding & ingestion · backups
Developmentadding read tools
SecuritySECURITY.md

Précis Finance MCP vs Précis

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).

Talk about Précis — the full platform and design-partner programme: precis.finance or [email protected].

Support and services

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.

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.

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