Startup Finance Metrics

io.github.MayankTalwar0由 GitHub 账号 MayankTalwar0 发布v1.1.2更新于 Oct 24, 2026

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

发布方自己的服务:Finance Foundation

已验证STDIO仅桌面金融数据与分析

概览

AI 生成的概览

一个本地运行的 MCP 服务器,用于计算初创公司的财务指标(如现金跑道、毛利率、CAC、LTV、Rule of 40),并生成 Markdown 与 HTML 报告。

可以做什么
  • 根据结构化输入计算初创公司财务指标,包括净烧钱率、现金跑道、毛利率、CAC、LTV、LTV:CAC、增长率、客户流失、Burn Multiple、NRR、Rule of 40 与 CAC 回收期
  • 数据缺失或含义不明时返回 insufficient_data 并列出缺少的输入,而不是猜测数值
  • 生成统一的 Markdown 与 HTML 报告,可针对单月,也可对多个月份做并排对比
  • 可基于银行、Stripe 或 QBO/Xero 的 CSV 导出或粘贴的数值,由助手把交易归类为收入、COGS、销售与市场、薪酬和 G&A
适用场景
适合需要在融资、董事会会议或投资人更新前快速得到一版指标报告的创始人和运营者,且希望数字在本机计算而非交给云端服务。
运行要求
通过 uvx 从 PyPI 包 startup-finance-mcp 以 stdio 在本地运行;需先安装 uv(它会自行下载 Python 依赖)。未声明任何账号、API 密钥或环境变量。输入以 JSON 参数提供,报告写入指定的输出目录。
安装前请注意
交易分类由 AI 完成,可能出错,分享结果前应核对分类。报告工具会向指定的输出目录写入文件。README 称数据仅在本地处理、不存储也不记录日志,且服务器不会更改任何财务状态。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Startup Finance Metrics,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

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.

来源:README.md,提交 69ab71c

工具

0
SourceWeft 尚未在沙箱中启动过该软件包,因此其工具未知。

版本历史

1
  1. v1.1.2最新Sep 16, 2026

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