StockValuation.io, a new open-source project spotted on Hacker News this week, aims to solve a real problem emerging as AI coding agents become more capable: how do you give an agent financial rigor without letting it hallucinate valuation numbers? The tool wraps Aswath Damodaran's valuation methodology into MCP (Model Context Protocol) tools that run locally via Docker, giving Codex and Claude CLI a deterministic DCF engine they can call rather than calculate themselves.
Why This Matters for Agent Workflows
The core insight behind StockValuation.io is architectural: research and math should stay separate. An AI agent excels at reading SEC filings, comparing sources, summarizing business models, and pressure-testing investment narratives. What it shouldn't do is hand-calculate a fair value or invent "evidence" to justify predetermined numbers. The project enforces this separation deliberately—agents handle evidence gathering and guided assumption questions while the local service handles DCF math and scenario recalculation.
Damodaran-Style Methodology in Practice
The approach follows Professor Aswath Damodaran's valuation philosophy, which prioritizes transparency over precision. In a Damodaran-style valuation, the chain from business story to assumptions to cash flows matters more than the final number. If growth projections increase, the model shows the revenue path. If margins expand, the report explains why. If reinvestment falls, users see the capital-efficiency claim behind it. StockValuation.io implements this with guided questions for material drivers—revenue growth, operating margin, reinvestment, risk, terminal value, and capital structure—before recalculating scenarios through the local service.
Technical Implementation
The project installs as a skill targeting Codex CLI and Claude by default, pulling from GitHub at https://github.com/stockvaluation-io/stockvaluation_io. A curl-based installer sets up both the agent skill and Docker services running the valuation runtime. Users can value companies with simple prompts like "Value MSFT using stockvaluation.io" or feed in SEC EDGAR prospectus URLs for filing-based analysis. The system extracts filing facts, pauses for evidence review, asks for assumption inputs, then produces an educational report marked clearly as non-financial advice.
What It Can't Do
The disclaimers are extensive by design. StockValuation.io does not support financial-sector companies (banks, insurance), may use normalized fallback data when primary filings lack coverage, and explicitly returns failures rather than polished numbers when upstream data is missing or unsuitable. Historical coverage has gaps, non-US and IFRS cases need extra source review, and the service acknowledges it doesn't ship a fully local LLM stack—inputs still come from external market data providers and company filings alongside whatever model powers your agent.
Key Takeaways
- Agents handle research; local tools handle DCF math—this separation is the architectural bet
- Damodaran methodology means visible assumptions and scenario chains, not black-box outputs
- MCP tool integration makes this composable with existing Codex/Claude workflows
- Educational focus: clear failures beat fake valuations when data can't support analysis
The Bottom Line
This is a thoughtful experiment in keeping AI agents honest about financial claims. Whether it gains traction depends on whether developers building agentic workflows prioritize rigor over convenience—but for anyone experimenting with coding agents that touch finance, StockValuation.io offers a principled framework worth studying.