Précis Finance MCP

Name: Précis Finance MCP
Author: precis-finance

제작자: precis-finance버전 0.4.0i★ –i클라우드에서 실행

레지스트리 이름io.github.precis-finance/precis-finance-mcp

설치

개요

제작자가 쓴 설명(영어 원문)

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

PopNx MCP의 세 기둥

이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.

사용자 의견i아직 리뷰가 없어요리뷰는 다음 단계에서 계정 기능과 함께 열려요
우리의 기본 점검i3개 중 3개 통과공개된 정보로 점검했으며, 코드는 아직 스캔하지 않았어요
사용 정도i아직 측정 안 함★ –

할 수 있는 일

이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.

연결 방식: 클라우드에서 실행
전송 방식: Streamable HTTP
카테고리: 회계, 재무 및 세금

설치

쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.

설치하기 전에

기본 점검: 3개 중 3개 통과
제작자의 서버에서 실행돼요. 내가 요청한 내용은 제작자의 서버를 거쳐요.
할 수 있는 일 20가지(도구) precis_orientation, list_scenarios, list_kpis, list_inspection_sources …
레지스트리에 입력해야 할 키가 나와 있지 않아요.

필요한 것

클라우드에서 실행설치할 것 없음. 인터넷으로 연결돼요

어떤 앱을 쓰세요?

ClaudeClaude CodeCursorVS CodeChatGPT

위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.

웹, 데스크톱, 모바일의 Claude에서 쓸 수 있어요:

Settings → Connectors 열기
Add custom connector 누르기
아래 주소를 붙여 넣고 Add 누르기
로그인하라고 하면 제작자의 서비스에 로그인하기

https://mcp.precis.finance/mcp

터미널에서 이 명령어를 실행하세요

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

Cursor에서 열기

또는 설정을 직접 복사하기

프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요

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

브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.

VS Code에서 열기

또는 설정을 직접 복사하기

프로젝트의 .vscode/mcp.json에 붙여 넣으세요

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

브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.

ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요:

웹에서 ChatGPT 열기 → Settings → Connectors (Apps)
Create 또는 Add 누르기(일부 요금제는 먼저 Advanced에서 Developer mode를 켜야 해요)
아래 주소를 붙여 넣고 이름을 정한 뒤 저장하기
로그인하라고 하면 제작자의 서비스에 로그인하기

https://mcp.precis.finance/mcp

메뉴 이름은 요금제나 시기에 따라 조금 다를 수 있어요.

선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.

이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.

할 수 있는 일

도구 이름과 설명은 서버가 2026년 11월 6일에 알려 준 그대로예요. 우리가 아니라 제작자가 쓴 글이고, 일반 텍스트로만 보여 드려요.

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.

기본 점검i

누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검

✓ 통과
제작자와 저장소 일치
레지스트리 이름이 GitHub 계정 precis-finance 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
… 아직 점검 안 함
계속 관리되고 있음
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
… 아직 점검 안 함
라이선스 있음
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
✓ 통과
엔드포인트 응답
516ms 만에 응답(HTTP 200)
– 해당 없음
OAuth로 로그인
로그인 필요 없음
✓ 통과
도구 설명에 숨겨진 지시 없음
도구 설명 20개를 읽었고, AI에게 보내는 숨겨진 지시는 없었어요

회계, 재무 및 세금 카테고리의 다른 서버와 비교i

이 카테고리의 다른 서버 2902개 중 0%보다 스타가 많아요
이 카테고리의 다른 서버 2902개 중 91%보다 최근에 레지스트리에 공개됐어요
기본 점검 결과가 이 카테고리의 다른 서버 2902개 중 100%와 같거나 더 좋아요

레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.

자주 묻는 질문

Précis Finance MCP, 안전한가요?

해당하는 기본 점검 3개 중 3개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.

어떤 키나 코드가 필요한가요?

레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.

내 컴퓨터에서 실행되나요, 클라우드에서 실행되나요?

제작자의 클라우드에서 실행돼요. 설치 없이 인터넷으로 연결하지만, 보내는 내용은 제작자의 서버를 거쳐요.

아직 관리되고 있나요?

GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.4.0)은 그저께 레지스트리에 올라왔어요.

어떤 앱에서 쓸 수 있나요?

Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. URL로 연결되기 때문에, 원격 MCP 서버를 지원하는 앱(예: 개발자 모드의 ChatGPT)에서도 쓸 수 있어요.

대신 쓸 수 있는 건 뭐가 있나요?

같은 카테고리에는: Finance Toolkit, SolidInvoice, Korea Tax Calculator

대안

같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.

카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i

Finance Toolkit

제작자: JerBouma

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

컴퓨터 또는 클라우드★ 5,496MIT어제 업데이트

설치

SolidInvoice

제작자: SolidInvoice

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

클라우드에서 실행★ 981MIT8일 전 업데이트

설치

Korea Tax Calculator

제작자: UpstageAI

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

내 컴퓨터에서 실행★ 97MIT어제 업데이트

설치

제작자의 README

README 열기

제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.

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

앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요

Précis Finance MCP

개요

PopNx MCP의 세 기둥

할 수 있는 일

설치

설치하기 전에

할 수 있는 일

기본 점검i

제작자와 저장소 일치

계속 관리되고 있음

라이선스 있음

엔드포인트 응답

OAuth로 로그인

도구 설명에 숨겨진 지시 없음

회계, 재무 및 세금 카테고리의 다른 서버와 비교i

자주 묻는 질문

대안

Finance Toolkit

SolidInvoice

Korea Tax Calculator

제작자의 README

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