Précis Finance MCP

Name: Précis Finance MCP
Author: precis-finance

de precis-financeversión 0.4.0i★ –iSe ejecuta en la nube

Nombre en el registroio.github.precis-finance/precis-finance-mcp

Instalar

Resumen

Descripción del creador (original en inglés)

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

Los tres pilares de PopNx MCP

Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.

Opinión de usuariosiAún no hay reseñasLas reseñas se abrirán junto con las cuentas en una etapa posterior
Nuestras revisiones básicasi3 de 3 superadasRevisado con información pública; el código aún no se ha escaneado
UsoiAún sin medir★ –

Qué puedes hacer con él

Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.

Conexión: Se ejecuta en la nube
Transporte: Streamable HTTP
Categoría: Contabilidad, finanzas e impuestos

Instalación

Elige la app de IA que usas y copia la configuración.

Antes de instalar

Revisiones básicas: 3 de 3 superadas
Se ejecuta en el servidor del creador: lo que le pidas pasa por su servidor.
Puede hacer 20 cosas (herramientas) precis_orientation, list_scenarios, list_kpis, list_inspection_sources …
El registro no indica ninguna clave que completar.

Necesitas

Se ejecuta en la nubeNo hay nada que instalar; se conecta por internet

¿Qué app usas?

ClaudeClaude CodeCursorVS CodeChatGPT

Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.

Funciona en Claude para web, escritorio y móvil:

Abre Settings → Connectors
Presiona Add custom connector
Pega la dirección de abajo y presiona Add
Si te pide iniciar sesión, inicia sesión en el servicio del creador

https://mcp.precis.finance/mcp

Ejecuta esto en tu terminal

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

Abrir en Cursor

O copia la configuración tú

Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json

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

Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.

Abrir en VS Code

O copia la configuración tú

Pégalo en .vscode/mcp.json de tu proyecto

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

Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.

ChatGPT solo se conecta a servidores en internet:

Abre ChatGPT en la web → Settings → Connectors (Apps)
Presiona Create o Add (en algunos planes primero hay que activar Developer mode en Advanced)
Pega la dirección de abajo, ponle un nombre y guarda
Si te pide iniciar sesión, inicia sesión en el servicio del creador

https://mcp.precis.finance/mcp

Los nombres de los menús pueden variar un poco según el plan y con el tiempo.

Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.

Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.

Qué puede hacer

Nombres y descripciones de las herramientas tal como los informó el servidor el 6 nov 2026. Los escribió el creador, no nosotros; los mostramos como texto sin formato.

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.
eval_chart_transform
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
export_dataset
Export 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-
get_file_download
Renew a download capability for an available, owned artifact.
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.

Revisiones básicasi

Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 8 nov 2026

✓ Superada
El creador coincide con el repo
El nombre en el registro se verificó con la cuenta de GitHub precis-finance, dueña del repo
… Aún sin revisar
Sigue en mantenimiento
Todavía no hemos leído este repo en GitHub
… Aún sin revisar
Tiene licencia
Todavía no hemos leído este repo en GitHub
✓ Superada
El endpoint responde
Respondió en 516 ms (HTTP 200)
– No aplica
Inicio de sesión con OAuth
No hace falta iniciar sesión
✓ Superada
Sin instrucciones ocultas en las descripciones de herramientas
Leímos 20 descripciones de herramientas y no encontramos instrucciones ocultas para la IA

Comparado con otros en Contabilidad, finanzas e impuestosi

Más estrellas que el 0% de los otros 2911 servidores de esta categoría
Publicado en el registro más recientemente que el 90% de los otros 2911 servidores de esta categoría
Revisiones básicas iguales o mejores que el 100% de los otros 2911 servidores de esta categoría

Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.

Preguntas frecuentes

¿Es seguro Précis Finance MCP?

Superó 3 de 3 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.

¿Qué claves o códigos necesita?

El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.

¿Se ejecuta en mi computadora o en la nube?

Se ejecuta en la nube del creador. Te conectas por internet sin instalar nada, pero lo que envías pasa por los servidores del creador.

¿Sigue en mantenimiento?

El repo de GitHub cambió por última vez –, y la última versión (0.4.0) llegó al registro anteayer.

¿Qué apps pueden usarlo?

Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. Se conecta por URL, así que también funcionan las apps que aceptan servidores MCP remotos, como ChatGPT en modo desarrollador.

¿Qué puedo usar en su lugar?

En la misma categoría: Finance Toolkit, SolidInvoice, Korea Tax Calculator

Alternativas

Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.

Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi

Finance Toolkit

de JerBouma

500+ transparent financial methods calculated from raw statements, not third-party endpoints.

Computadora o nube★ 5496MITActualizado ayer

Instalar

SolidInvoice

de SolidInvoice

Create and send invoices and quotes, manage clients, and track payments in SolidInvoice.

Se ejecuta en la nube★ 981MITActualizado hace 8 días

Instalar

Korea Tax Calculator

de UpstageAI

Korean tax calculation and tax-audit judgment MCP: 96 tools with statutory citations.

Se ejecuta en tu computadora★ 97MITActualizado anteayer

Instalar

README del creador

Abrir el README

Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.

Précis Finance MCP — Financial reporting on Databricks and ClickHouse

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.

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 or Databricks? — optionally point the engine at your own instance instead of the bundled one; see ClickHouse data modes or Databricks setup.
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

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 hello@precis.finance.

Multi-user deployment

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:

Data — bundled ClickHouse (empty or with sample data), your own ClickHouse instance, or Databricks: ClickHouse data modes · Databricks setup
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. For Databricks, see the Databricks schema contract. 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 — the agent requests defined metrics; Précis calculates and returns them using the agreed financial rules. Ask for utilisation by department and you get each department’s utilisation, with a total calculated using the same governed definition. Other agentic analytics approaches rely on agents writing SQL. When an agent writes SQL for each question, it must interpret how the measure should be calculated and aggregated, introducing opportunities for errors. Précis defines that logic once in the reporting engine and applies it consistently as the conversation develops. The agent retrieves the figures and narrates the results.
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.

FP&A and financial reporting on Databricks

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.

Documentation

Full documentation: docs.precis.finance.

Area	Start here
Getting started	quickstart
Deployment	ClickHouse data modes · Databricks setup · OAuth / Keycloak · external IdP recipes
Excel

Solo se muestra el principio; lee el resto en el repo

Précis Finance MCP

Resumen

Los tres pilares de PopNx MCP

Qué puedes hacer con él

Instalación

Antes de instalar

Qué puede hacer

Revisiones básicasi

El creador coincide con el repo

Sigue en mantenimiento

Tiene licencia

El endpoint responde

Inicio de sesión con OAuth

Sin instrucciones ocultas en las descripciones de herramientas

Comparado con otros en Contabilidad, finanzas e impuestosi

Preguntas frecuentes

Alternativas

Finance Toolkit

SolidInvoice

Korea Tax Calculator

README del creador

Précis Finance MCP — Financial reporting on Databricks and ClickHouse

Use this if

Requirements

What Précis Finance MCP looks like

Try the public demo

Quickstart — single-user local trial

Multi-user deployment

Configure your data model

Security and read-only posture

FP&A and financial reporting on Databricks

Documentation