I treated my CV like a data product-evidence.json,MCP endpoint,llms.txt
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Unlocked Japanese financial data—XBRL parsed to JSON for 11k companies, no signup.
Quants, fintech engineers, researchers analyzing Japanese equities; no signup required to start.
Alpha Vantage · Finnhub · SEC Edgar API
Japan is the world's 4th largest stock market, but its financial data is stuck in the 90s. Corporate filings go through EDINET — a government system that stores everything as XBRL in Japanese, with 3 different accounting standards (JP-GAAP,IFRS, US-GAAP), company-specific taxonomies, and no usable API.
I've been working with Japanese XBRL filings for 2 years. I built a parser that normalizes all of it into structured JSON. Axiora now covers 4,125 listed companies with ~10 years of history and 52 financial fields per company-year.
Built for developers:
· REST API with full OpenAPI spec — https://axiora.dev/docs · Python SDK: pip install axiora · LangChain integration: pip install langchain-axiora (20 tools) · MCP server for Claude, ChatGPT, Cursor, etc. · llms.txt at axiora.dev/llms.txt
Try it now, no signup: https://axiora.dev/terminalRoadmap: TDNet integration (real-time timely disclosures from JPX) and stock prices are next. Data licenses are expensive and I'm bootstrapping this solo, so those will come as paid tiers.
Tech: Python/FastAPI on Cloud Run, Supabase (Postgres), lxml for XBRL parsing.
Free tier: 10,000 req/day. I'd love feedback on the API design or what data would be most useful.
Evidence-mapped CV beats PDF for AI recruiter parsing, but applies only to ATS that read these formats.
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