Startup Finance Metrics

What is Startup Finance Metrics?

Startup Finance Metrics is an MCP server for analyzing startup financial health locally. It ingests bank statements, Stripe/QBO exports, or pasted values, uses AI to categorize transactions, computes key metrics (net burn, runway, gross margin, CAC, LTV, etc.), and generates unified Markdown and HTML reports. It is designed for startup founders and operators who need fast, on‑machine financial analysis without sending sensitive data to external services.

How to use Startup Finance Metrics?

Install uv (curl/powershell commands provided), then configure your MCP client (e.g., Claude Desktop or Claude Code) with the command uvx startup-finance-mcp. The server exposes two tools: computeFinancialMetrics for computing metrics per month, and generateFinancialReport for producing a comparative report across one or more months. For manual prompting, a standalone skill prompt is available in skills/SKILL.md.

Key features of Startup Finance Metrics

• 100% local processing – zero cloud risk, no data sent externally
• No data storage – processes in‑memory, returns results to client only
• Ingests bank CSVs, Stripe exports, QBO/Xero exports, or pasted values
• AI‑driven transaction categorization into revenue, COGS, S&M, payroll, G&A
• Computes 12+ startup metrics (Net Burn, Runway, Gross Margin, CAC, LTV, Rule of 40, etc.)
• Generates unified Markdown and HTML comparative reports

Use cases of Startup Finance Metrics

• Founders preparing investor reports or board meeting materials
• Rapidly building a structured financial metrics report from raw bank statements
• Checking cash runway, burn rate, and growth metrics during fundraising
• Validating financial health before sharing data with investors – no data leaves your machine

FAQ from Startup Finance Metrics

Does my financial data leave my computer?

No. The server runs entirely locally, processes inputs in memory, and never sends data to any external API, cloud provider, or third‑party service (including SlickBooks). No data is stored, cached, or logged.

Can I trust the AI transaction categorizations?

The AI categorizes transactions into revenue, COGS, S&M, payroll, or G&A based on descriptions. This step can make mistakes (e.g., misclassifying a contractor payment), so you should always review categorizations before sharing results with investors.

What data do I need to provide?

For best results, supply a minimum of three months of bank statements and active user stats. Sample files are available in the test/ folder. The server will return insufficient_data with missing_inputs if required data is missing or ambiguous.

What are the runtime dependencies?

The server requires uv (a fast Python manager). Python does not need to be pre‑installed – uv automatically downloads everything needed in the background. It works with Claude Desktop, Claude Code, Glama, Cursor, and other MCP clients.

What metrics does Startup Finance Metrics compute?

It calculates 12 metrics including Net Burn, Runway, Gross Margin, CAC, LTV, LTV:CAC, Revenue Growth, Logo Churn, Burn Multiple, NRR, Rule of 40, and CAC Payback. Each metric’s formula and required inputs are listed in the README’s metrics reference table.