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Feat: Industry-specific metrics via SIC code detection + company address
- Add SIC-to-sector mapping for 13 industries (config.py) - Add industry-specific XBRL concept mappings for: Insurance, Banks, REITs, Oil & Gas, Utilities, Technology, Healthcare, Retail, Financials, Industrials, Transportation, Materials, Mining - Add 60+ industry-specific fields to ParsedFinancials schema - Extract industry metrics based on detected sector (parser.py) - Add business_address extraction from SEC EDGAR submissions - Include company info (with address) in get_all_sources_fundamentals - Add Company Info section to E2E test report Tested with CVX (Oil & Gas), V (Financials), L (Insurance) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>ab2f4fb -
Fix: Standardize MCP server timeouts to 90s and use subprocess+MCP in tests
- Remove server_legacy.py (unused direct import path) - Update test_mcp_e2e.py to use subprocess+MCP protocol (same as production) - Standardize TOOL_TIMEOUT to 90s across all MCP servers to match mcp_client - fundamentals: 60s -> 90s - valuation: 45s -> 90s - volatility: 45s -> 90s - macro: 45s -> 90s (was causing timeouts on HuggingFace) - news: 45s -> 90s - sentiment: 60s -> 90s Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>d377800
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Add schema normalizers for MCP output consistency
- Add _normalize_fundamentals(), _normalize_valuation(), _normalize_volatility(), _normalize_macro() - Flatten sources wrapper to top-level keys (sec_edgar, yahoo_finance, etc.) - Rename financials to fundamentals for consistency across codebase - Add docs: metrics_schema_emitted.md, schema_normalization.md Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>26c5c2f