Startup Finance Metrics

io.github.MayankTalwar0由 GitHub 帳號 MayankTalwar0 發布v1.1.2更新於 Oct 24, 2026

An MCP server for analyzing startup financial health and generating metrics reports locally.

發布方自己的服務:Finance Foundation

已驗證STDIO僅桌面金融資料與分析

概覽

AI 產生的概覽

一個在本機執行的 MCP 伺服器,用來計算新創公司的財務指標(如現金跑道、毛利率、CAC、LTV、Rule of 40),並產生 Markdown 與 HTML 報告。

可以做什麼
  • 依結構化輸入計算新創公司財務指標,包括淨燒錢率、現金跑道、毛利率、CAC、LTV、LTV:CAC、成長率、客戶流失、Burn Multiple、NRR、Rule of 40 與 CAC 回收期
  • 資料缺漏或語意不明時回傳 insufficient_data 並列出缺少的輸入,而不是自行猜測數值
  • 產生統一的 Markdown 與 HTML 報告,可針對單一月份,也可對多個月份做並排比較
  • 可依據銀行、Stripe 或 QBO/Xero 的 CSV 匯出檔或貼上的數值,由助理將交易歸類為營收、COGS、銷售與行銷、人事薪資及 G&A
適用情境
適合需要在募資、董事會會議或投資人更新前快速取得一版指標報告的創辦人與營運人員,且希望數字在自己的電腦上計算,而非交給雲端服務。
執行需求
透過 uvx 從 PyPI 套件 startup-finance-mcp 以 stdio 在本機執行;需先安裝 uv(它會自行下載 Python 相依套件)。未宣告任何帳號、API 金鑰或環境變數。輸入以 JSON 參數提供,報告會寫入指定的輸出目錄。
安裝前請注意
交易分類由 AI 完成,可能出錯,分享結果前應先核對分類。報告工具會將檔案寫入指定的輸出目錄。README 表示資料僅在本機處理、不儲存也不記錄日誌,且伺服器不會變更任何財務狀態。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Startup Finance Metrics,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照 儲存庫 中的啟動說明。

README

Startup Finance Metrics (MCP Server)

An MCP (Model Context Protocol) server for analyzing startup financial health and generating metrics reports locally.

🔒 PRIVACY & SECURITY FIRST:

  • Zero Cloud Risk: This tool runs 100% locally on your machine/server.
  • No Data Sent Externally: Financial data is NEVER sent to any external API, cloud provider, or third-party service (including SlickBooks).
  • No Data Storage: The server processes inputs in-memory and returns the metrics directly to the MCP client. No data is stored, cached, or logged.
  • Strictly Read-Only: This server executes NO financial state changes. It is a strictly read-only mathematical engine.
  • Strictly Local Processing: Safely integrates with Claude Desktop, Cursor, Glama, and other MCP clients while maintaining full data sovereignty over your sensitive financial inputs.

Why This Exists

If you're a startup founder raising funds or preparing for a board meeting, investors will ask you for metrics like MRR, burn rate, gross margin, LTV:CAC, and runway — often on short notice. Most founders either don't track these consistently, or spend hours pulling numbers from bank statements and spreadsheets before every fundraise.

This tool turns your raw bank statement (or Stripe/QBO export) into a structured financial metrics report in minutes, entirely on your own machine. No accountant required for a first pass. No sensitive data leaving your computer.

What It Does

  1. Ingests Data: Accepts bank CSVs, Stripe export CSVs, QBO/Xero export CSVs, or pasted values. (For best results, provide a minimum 3-month bank statement and active user stats. Sample files are available in the test/ folder).
  2. AI Transaction Categorization: The AI classifies each bank transaction into revenue, COGS, S&M, payroll, or G&A based on the description. This step is AI-driven and can make mistakes — e.g. misclassifying a contractor payment as payroll vs. COGS, or missing an ambiguous line item. Always review the categorizations before sharing results with investors.
  3. Computes Key Metrics: Calculates Net Burn, Runway, Gross Margin, CAC, LTV, Rule of 40, and more — across one or multiple months in a single comparative report.
  4. Strict Validation: Returns insufficient_data with missing_inputs instead of hallucinating values. If data is missing or ambiguous, the engine tells you what's needed rather than guessing.
  5. Generates Reports: Creates clean, formatted Markdown and HTML reports — one unified report covering all months supplied, with side-by-side period comparison.

mcp-name: io.github.MayankTalwar0/startup-finance-metrics

Setup & Installation

Option 1: Claude Desktop (Manual Installation for Non-Developers)

Since this tool runs entirely on your own machine to protect your financial data, it requires a one-time manual setup. Good News: You do NOT need to have Python installed! The tool we use below (uv) will automatically download everything it needs invisibly in the background.

Step 1: Install uv This server uses uv (a fast Python manager) to run locally. If you don't have it installed:

  • Mac/Linux: Open your Terminal and run: curl -LsSf https://astral.sh/uv/install.sh | sh
  • Windows: Open PowerShell and run: powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Step 2: Open Claude's Configuration

  1. Open the Claude Desktop App.
  2. In the top left menu, click Claude -> Settings (or Preferences).
  3. Click on the Developer tab in the left sidebar.
  4. Click the Edit Config button. This will open a file named claude_desktop_config.json in your default text editor.

Step 3: Add the Server Replace the contents of that file with the following code (if you already have other servers, just add the startup-finance-metrics block inside your existing mcpServers):

json
{  "mcpServers": {    "startup-finance-metrics": {      "command": "uvx",      "args": [        "startup-finance-mcp"      ]    }  }}

Step 4: Restart Claude Save the file, close it, and completely restart Claude Desktop. You will now see a new "hammer" (Tools) icon in your Claude chats!

Option 2: Claude Code, Glama, or Custom Cursor setup

For CLI agents like Claude Code, or if you prefer to manually configure Glama and Cursor, use the uvx command:

For Claude Code:

bash
claude mcp add startup-finance -- uvx startup-finance-mcp

For Glama / Cursor (Custom MCP config):

bash
uvx startup-finance-mcp

Option 3: Local Development

bash
git clone https://github.com/MayankTalwar0/startup-finance-metrics.gitcd startup-finance-metricspip install -e .
# Run the server directlystartup-finance-mcp

Available MCP Tools

This server provides the following tools to the MCP client:

  1. computeFinancialMetrics(inputs_json: str): Computes startup financial metrics (runway, gross margin, CAC, LTV, etc.) from structured inputs. Called once per month when analyzing multi-month data.
  2. generateFinancialReport(metrics_json: str, output_dir: str): Renders a unified HTML + Markdown report. Accepts either a single-month payload or a multi-month {"months": [...]} payload — producing one comparative report across all periods supplied.

Using as a Standalone AI Skill

If you don't want to use the full MCP server and just want a simple prompt to use in tools like Claude Code or OpenClaw, you can find the raw skill prompt in skills/SKILL.md.

Metrics Reference

#MetricFormulaRequired inputs
1Net Burnmonthly_opex - monthly_revenuemonthly_opex, monthly_revenue
2Runwaycurrent_cash / net_burncurrent_cash; requires net_burn > 0 (else returns not_applicable: business is cash flow positive)
3Gross Margin(monthly_revenue - cogs) / monthly_revenue * 100monthly_revenue, cogs
4CACsales_marketing_spend / new_customerssales_marketing_spend, new_customers
5LTV(ARPU * gross_margin) / logo_churn_ratemonthly_revenue, active_customers, lost_customers, cogs
6LTV:CACltv / cacComputable ltv, computable cac
7Revenue Growth(monthly_revenue - prev_monthly_revenue) / prev_m... * 100monthly_revenue, prev_monthly_revenue
8Logo Churnlost_customers / active_customers * 100lost_customers, active_customers
9Burn Multiplenet_burn / (arr_end - arr_start)monthly_opex, monthly_revenue, arr_start, arr_end
10NRR(start + exp - churn - cont) / start * 100starting_mrr, expansion_mrr, churned_mrr, contraction_mrr
11Rule of 40revenue_growth_yoy_pct + operating_margin_pctrevenue_growth_yoy_pct, operating_margin_pct
12CAC Paybackcac / (ARPU * gross_margin)Computable cac, monthly_revenue, active_customers, computable gross_margin

License

MIT

Built By SlickBooks

Built by Mayank, founder of SlickBooks. SlickBooks provides managed bookkeeping, bookkeeping automation, financial forecast automation, and custom finance agents.

來源:README.md,提交 69ab71c

工具

0
SourceWeft 尚未在沙箱中啟動過該軟體包,因此其工具未知。

版本歷史

1
  1. v1.1.2最新Sep 16, 2026

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