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>

docs/mcp_test_report_CVX.md

@@ -0,0 +1,71 @@
+# MCP E2E Test Report: Chevron Corporation (CVX)
+
+## Summary
+
+| S/N | MCP | Status | Expected | Actual | Duration | Errors | Warnings |
+|-----|-----|--------|----------|--------|----------|--------|----------|
+| 1 | fundamentals | PASS | 9 | 11 | 12114ms | - | - |
+| 2 | valuation | PASS | 11 | 11 | 8105ms | - | - |
+| 3 | volatility | PASS | 5 | 5 | 5789ms | - | - |
+| 4 | macro | PASS | 4 | 4 | 7236ms | - | - |
+| 5 | news | PASS | - | 4 | 6433ms | - | - |
+| 6 | sentiment | PASS | - | 55 | 5084ms | - | - |
+
+---
+
+## Quantitative Data
+
+| S/N | Metric | Value | Data Type | As Of | Source | Category |
+|-----|--------|-------|-----------|-------|--------|----------|
+| 1 | revenue | 193414000000 | FY | 2024-12-31 | SEC EDGAR | Fundamentals |
+| 2 | net_income | 17661000000 | FY | 2024-12-31 | SEC EDGAR | Fundamentals |
+| 3 | net_margin_pct | 9.13 | FY | 2024-12-31 | SEC EDGAR | Fundamentals |
+| 4 | total_assets | 256938000000 | FY | 2024-12-31 | SEC EDGAR | Fundamentals |
+| 5 | total_liabilities | 103781000000 | FY | 2024-12-31 | SEC EDGAR | Fundamentals |
+| 6 | stockholders_equity | 152318000000 | FY | 2024-12-31 | SEC EDGAR | Fundamentals |
+| 7 | oil_gas_revenue | 193414000000 | FY | 2024-12-31 | SEC EDGAR | Fundamentals |
+| 8 | depletion | 17282000000 | FY | 2024-12-31 | SEC EDGAR | Fundamentals |
+| 9 | total_debt | 41543999488 | Point-in-time | 2025-09-30 | Yahoo Finance | Fundamentals |
+| 10 | operating_cash_flow | 31844999168 | TTM | 2025-09-30 | Yahoo Finance | Fundamentals |
+| 11 | free_cash_flow | 15743875072 | TTM | 2025-09-30 | Yahoo Finance | Fundamentals |
+| 12 | current_price | 162.11 | - | 2026-01-12 | yahoo_finance | Valuation |
+| 13 | market_cap | 326622871552.0 | - | 2026-01-12 | yahoo_finance | Valuation |
+| 14 | enterprise_value | 365985988608.0 | - | 2026-01-12 | yahoo_finance | Valuation |
+| 15 | trailing_pe | 22.76826 | - | 2026-01-12 | yahoo_finance | Valuation |
+| 16 | forward_pe | 22.051102 | - | 2026-01-12 | yahoo_finance | Valuation |
+| 17 | ps_ratio | 1.73103 | - | 2026-01-12 | yahoo_finance | Valuation |
+| 18 | pb_ratio | 1.7193798 | - | 2026-01-12 | yahoo_finance | Valuation |
+| 19 | trailing_peg | 3.1673 | - | 2026-01-12 | yahoo_finance | Valuation |
+| 20 | forward_peg | - | - | 2026-01-12 | yahoo_finance | Valuation |
+| 21 | earnings_growth | -0.266 | - | 2026-01-12 | yahoo_finance | Valuation |
+| 22 | revenue_growth | -0.014 | - | 2026-01-12 | yahoo_finance | Valuation |
+| 23 | vix | 15.45 | Daily | 2026-01-08 | FRED (Federal Reserve) | Volatility |
+| 24 | vxn | 20.15 | Daily | 2026-01-08 | FRED (Federal Reserve) | Volatility |
+| 25 | beta | 0.683 | 1Y | 2026-01-09 | Calculated from Yahoo Finance data | Volatility |
+| 26 | historical_volatility | 27.6 | 30D | 2026-01-09 | Calculated from Yahoo Finance data | Volatility |
+| 27 | implied_volatility | 30.0 | Forward | 2026-01-12 | Market Average (estimated) | Volatility |
+| 28 | gdp_growth | 4.3 | Quarterly | 2025Q3 | BEA (Bureau of Economic Analysis) | Macro |
+| 29 | interest_rate | 3.72 | Monthly | 2025-12-01 | FRED (Federal Reserve) | Macro |
+| 30 | cpi_inflation | 2.74 | Monthly | 2025-November | BLS (Bureau of Labor Statistics) | Macro |
+| 31 | unemployment | 4.4 | Monthly | 2025-December | BLS (Bureau of Labor Statistics) | Macro |
+
+---
+
+## Qualitative Data
+
+| S/N | Title | Date | Source | Subreddit | URL | Category |
+|-----|-------|------|--------|-----------|-----|----------|
+| 1 | Chevron Corporation (CVX) Latest Stock News & Headlines | - | Tavily | - | [Link](https://finance.yahoo.com/quote/CVX/news/) | News |
+| 2 | CVX: Chevron Corp - Stock Price, Quote and News | - | Tavily | - | [Link](https://www.cnbc.com/quotes/CVX) | News |
+| 3 | CVX Chevron Corporation Stock Price & Overview | - | Tavily | - | [Link](https://seekingalpha.com/symbol/CVX) | News |
+| 4 | Chevron - CVX - Stock Price & News | - | Tavily | - | [Link](https://www.fool.com/quote/nyse/cvx/) | News |
+| 5 | Trump Pushes Venezuela Oil Investment as Political Risks Loom | 2026-01-12 | Finnhub | - | [Link](https://finnhub.io/api/news?id=4bd9554c63299a6d185eb386ac66113a65a3c2142538b39c2e37d66b773dba22) | Sentiment |
+| 6 | Chevron Corporation's (NYSE:CVX) Stock On An Uptrend: Could Fundamentals Be Driv | 2026-01-12 | Finnhub | - | [Link](https://finnhub.io/api/news?id=e73167fa7eed0ebaa552782f612d05724c9bcafa6fd13b2a1ebc2cd040383b13) | Sentiment |
+| 7 | Trump's magic number in Venezuela is oil at $50 per barrel | 2026-01-12 | Finnhub | - | [Link](https://finnhub.io/api/news?id=00eea749a137e2ca61c5570e7caf68884968914395d224e7cf5a9b3402ffbf48) | Sentiment |
+| 8 | Trump ‘Inclined’ to Keep Exxon Out of Venezuela | 2026-01-12 | Finnhub | - | [Link](https://finnhub.io/api/news?id=e8a561541ca1dd06a87ce7602ca45dde4b4f6c902655bb814d58614f86224f66) | Sentiment |
+| 9 | Energy Stocks: Winners And Losers At The Start Of 2026 | 2026-01-11 | Finnhub | - | [Link](https://finnhub.io/api/news?id=fc0a691fdf514129ccf8302385630e7864f71b5a93797d8bd6dfc7a3f95eb1d6) | Sentiment |
+| 10 | Energy Secretary Says at Least a Dozen Oil Companies Eager for Venezuela | 2026-01-11 | Finnhub | - | [Link](https://finnhub.io/api/news?id=ac097cc854e23441d94bba402ebd895d68fe4209ee559a614a813bbdc892d913) | Sentiment |
+| 11 | Energy Is Still My No. 1 Buy - Even With Venezuela, Politics, And Everything Els | 2026-01-11 | Finnhub | - | [Link](https://finnhub.io/api/news?id=d6fee5c4b9143c17c124b39b2089f158e9763ef335413be7c41afa0f3ecfa69c) | Sentiment |
+| 12 | DLN: Diversified Large Value ETF With Risk Screening | 2026-01-11 | Finnhub | - | [Link](https://finnhub.io/api/news?id=f22b9cd12374143fe7f0d9443a755c3efd39b084c1d28926cea65f46dc33a445) | Sentiment |
+| 13 | Jim Mellon Says Venezuela's Oil Recovery Is 5+ Years Away, But US Refiners Could | 2026-01-10 | Finnhub | - | [Link](https://finnhub.io/api/news?id=0ca15289a90e3799333ef758956f7336c1d7afb55a6c734b5cb534ff3aab7a4e) | Sentiment |
+| 14 | Can Chevron Stock Hit $205 in 2026? | 2026-01-10 | Finnhub | - | [Link](https://finnhub.io/api/news?id=20d3d06abcd6c11df1288a9eec97e52017ecabd73d7d9600b21b19d1b344840f) | Sentiment |

docs/mcp_test_report_V.md

@@ -0,0 +1,87 @@
+# MCP E2E Test Report: Visa Inc. (V)
+
+## Summary
+
+| S/N | MCP | Status | Expected | Actual | Duration | Errors | Warnings |
+|-----|-----|--------|----------|--------|----------|--------|----------|
+| 1 | fundamentals | PASS | 9 | 11 | 26990ms | - | - |
+| 2 | valuation | PASS | 11 | 11 | 8717ms | - | - |
+| 3 | volatility | PASS | 5 | 5 | 5323ms | - | - |
+| 4 | macro | PASS | 4 | 4 | 7508ms | - | - |
+| 5 | news | PASS | - | 6 | 4933ms | - | - |
+| 6 | sentiment | PASS | - | 56 | 5479ms | - | - |
+
+---
+
+## Company Info
+
+| Field | Value |
+|-------|-------|
+| Name | VISA INC. |
+| CIK | 0001403161 |
+| SIC | 7389 (Services-Business Services, NEC) |
+| State | DE |
+| Fiscal Year End | 0930 |
+| Address | P.O. BOX 8999 |
+| | SAN FRANCISCO, CA 94128-8999 |
+
+---
+
+## Quantitative Data
+
+| S/N | Metric | Value | Data Type | As Of | Source | Category |
+|-----|--------|-------|-----------|-------|--------|----------|
+| 1 | revenue | 40000000000 | FY | 2025-09-30 | SEC EDGAR | Fundamentals |
+| 2 | net_income | 20058000000 | FY | 2025-09-30 | SEC EDGAR | Fundamentals |
+| 3 | net_margin_pct | 50.14 | FY | 2025-09-30 | SEC EDGAR | Fundamentals |
+| 4 | total_assets | 99627000000 | FY | 2025-09-30 | SEC EDGAR | Fundamentals |
+| 5 | total_liabilities | 61718000000 | FY | 2025-09-30 | SEC EDGAR | Fundamentals |
+| 6 | stockholders_equity | 26437000000 | FY | 2011-09-30 | SEC EDGAR | Fundamentals |
+| 7 | deferred_revenue | 81000000 | FY | 2015-09-30 | SEC EDGAR | Fundamentals |
+| 8 | goodwill | 19879000000 | FY | 2025-09-30 | SEC EDGAR | Fundamentals |
+| 9 | total_debt | 26083999744 | Point-in-time | 2025-09-30 | Yahoo Finance | Fundamentals |
+| 10 | operating_cash_flow | 23058999296 | TTM | 2025-09-30 | Yahoo Finance | Fundamentals |
+| 11 | free_cash_flow | 20072873984 | TTM | 2025-09-30 | Yahoo Finance | Fundamentals |
+| 12 | current_price | 339.78 | - | 2026-01-12 | yahoo_finance | Valuation |
+| 13 | market_cap | 655898312704.0 | - | 2026-01-12 | yahoo_finance | Valuation |
+| 14 | enterprise_value | 677386649600.0 | - | 2026-01-12 | yahoo_finance | Valuation |
+| 15 | trailing_pe | 33.287148 | - | 2026-01-12 | yahoo_finance | Valuation |
+| 16 | forward_pe | 23.56721 | - | 2026-01-12 | yahoo_finance | Valuation |
+| 17 | ps_ratio | 16.393513 | - | 2026-01-12 | yahoo_finance | Valuation |
+| 18 | pb_ratio | 17.534918 | - | 2026-01-12 | yahoo_finance | Valuation |
+| 19 | trailing_peg | 1.9228 | - | 2026-01-12 | yahoo_finance | Valuation |
+| 20 | forward_peg | - | - | 2026-01-12 | yahoo_finance | Valuation |
+| 21 | earnings_growth | -0.014 | - | 2026-01-12 | yahoo_finance | Valuation |
+| 22 | revenue_growth | 0.115 | - | 2026-01-12 | yahoo_finance | Valuation |
+| 23 | vix | 14.49 | Daily | 2026-01-09 | FRED (Federal Reserve) | Volatility |
+| 24 | vxn | 19.06 | Daily | 2026-01-09 | FRED (Federal Reserve) | Volatility |
+| 25 | beta | 0.787 | 1Y | 2026-01-12 | Calculated from Yahoo Finance data | Volatility |
+| 26 | historical_volatility | 23.82 | 30D | 2026-01-12 | Calculated from Yahoo Finance data | Volatility |
+| 27 | implied_volatility | 30.0 | Forward | 2026-01-12 | Market Average (estimated) | Volatility |
+| 28 | gdp_growth | 4.3 | Quarterly | 2025Q3 | BEA (Bureau of Economic Analysis) | Macro |
+| 29 | interest_rate | 3.72 | Monthly | 2025-12-01 | FRED (Federal Reserve) | Macro |
+| 30 | cpi_inflation | 2.74 | Monthly | 2025-November | BLS (Bureau of Labor Statistics) | Macro |
+| 31 | unemployment | 4.4 | Monthly | 2025-December | BLS (Bureau of Labor Statistics) | Macro |
+
+---
+
+## Qualitative Data
+
+| S/N | Title | Date | Source | Subreddit | URL | Category |
+|-----|-------|------|--------|-----------|-----|----------|
+| 1 | Big Tech stocks are getting cheaper, and that could mean gains of up to 60% | 2025-12-16 | MarketWatch | - | [Link](https://www.marketwatch.com/story/big-tech-stocks-are-getting-cheaper-and-that-could-mean-gains-of-up-to-60-fdf1b70c) | News |
+| 2 | Dow, S&P 500 end at records because investors feel good about the economy — beyo | 2025-12-11 | MarketWatch | - | [Link](https://www.marketwatch.com/story/dow-s-p-500-end-at-records-because-investors-feel-good-about-the-economy-beyond-the-ai-boom-0dcad0b9) | News |
+| 3 | Visa Inc. (V) Stock Price, News, Quote & History | - | Tavily | - | [Link](https://ca.finance.yahoo.com/quote/V/) | News |
+| 4 | V: Visa Inc - Stock Price, Quote and News | - | Tavily | - | [Link](https://www.cnbc.com/quotes/V) | News |
+| 5 | Is Visa Inc. (V) One of the Best Major Stocks to Invest in ... | - | Tavily | - | [Link](https://finance.yahoo.com/news/visa-inc-v-one-best-092151784.html) | News |
+| 6 | Visa Inc. (V) Stock Price, Quote, News & Analysis | - | Tavily | - | [Link](https://seekingalpha.com/symbol/V) | News |
+| 7 | Capital One, Credit Cards Dive As Trump Aims To Cap Interest Rates. | 2026-01-12 | Finnhub | - | [Link](https://finnhub.io/api/news?id=f14f8c1ccfdda6a9068faa37a8dde58ea9101ed9679737fd209638d779a84143) | Sentiment |
+| 8 | JPMorgan, Visa Stocks Fall After Trump Calls for Credit-Card Rate Cap | 2026-01-12 | Finnhub | - | [Link](https://finnhub.io/api/news?id=dfaff595a6a512f56a475e0d1b62e7f95f5c4a7c8a793000929d7bb6d2072e98) | Sentiment |
+| 9 | Stocks Fall Pre-Bell as Fed Chair Powell Faces Department of Justice Probe | 2026-01-12 | Finnhub | - | [Link](https://finnhub.io/api/news?id=dc954fbc0276d687ba5f759a9f5a19458cb9e6ac646bc4250440abbdd840b7f0) | Sentiment |
+| 10 | Latest News In Digital Payment - Euronet Expands Through Strategic CrediaBank Pa | 2026-01-12 | Finnhub | - | [Link](https://finnhub.io/api/news?id=a80fd83da78d5f30d43512e15e0dd3fa414afc813a8457c405db530fc1f1c884) | Sentiment |
+| 11 | FIS Launches Industry-First Offering Enabling Banks to Lead and Scale in Agentic | 2026-01-12 | Finnhub | - | [Link](https://finnhub.io/api/news?id=a96f73e317961b213a9ca0f890d2b14b55ff554dc426401b7df31bf50de9de10) | Sentiment |
+| 12 | Major credit card stocks slide after Trump comments on credit card rates | 2026-01-12 | Finnhub | - | [Link](https://finnhub.io/api/news?id=83c74a108744a4273876ed809513a71ef3db9d71ec403ecf36a52f0540cb584f) | Sentiment |
+| 13 | If I Were Starting A Dividend Portfolio In 2026, Here's How I Would Invest | 2026-01-12 | Finnhub | - | [Link](https://finnhub.io/api/news?id=32f4909f469af6dff4c17103f9119e7f14fce514a3beabf244b99a535852eee7) | Sentiment |
+| 14 | 2 Top Dividend Stocks I'd Own Over the Next Decade | 2026-01-11 | Finnhub | - | [Link](https://finnhub.io/api/news?id=5bbdac3350f76a959bd87fa2e497cc760a4b6bb20d30a36f8af8b1cd67ccefd2) | Sentiment |
+| 15 | 3 Dividend Stocks to Buy in 2026 and Hold Forever | 2026-01-11 | Finnhub | - | [Link](https://finnhub.io/api/news?id=60600fbee5d92497ffb0a1450483cacc710a22c0be92f8454b9b9772023b4ca8) | Sentiment |
+| 16 | Does Trump’s 10% Credit Card Rate Cap Make Visa and Mastercard a Buy? | 2026-01-10 | Finnhub | - | [Link](https://finnhub.io/api/news?id=b30fc6d903d414f158b9367342c05ed0f355815db628416a2118ae29aa55edf3) | Sentiment |

mcp-servers/fundamentals-basket/config.py

@@ -141,3 +141,260 @@ INSTANCE_PORTS = [8001, 8002, 8003]
 
 # Instance identification
 INSTANCE_ID = os.getenv("INSTANCE_ID", f"financials-default")
+
+# =============================================================================
+# INDUSTRY CLASSIFICATION (SIC Code Mapping)
+# =============================================================================
+
+# Specific 4-digit SIC codes that need special handling
+SIC_SPECIFIC_MAP = {
+    "6798": "REAL_ESTATE",   # Real Estate Investment Trusts (REITs)
+}
+
+# First 2 digits of SIC → Sector
+SIC_SECTOR_MAP = {
+    # Financials
+    "60": "BANKS",           # Depository Institutions
+    "61": "BANKS",           # Non-depository Credit
+    "62": "FINANCIALS",      # Securities & Commodities
+    "63": "INSURANCE",       # Insurance Carriers
+    "64": "INSURANCE",       # Insurance Agents
+    "65": "REAL_ESTATE",     # Real Estate
+    "67": "FINANCIALS",      # Holding & Investment (except 6798 REITs)
+
+    # Energy
+    "10": "MINING",
+    "12": "MINING",
+    "13": "OIL_GAS",         # Oil & Gas Extraction
+    "29": "OIL_GAS",         # Petroleum Refining
+    "49": "UTILITIES",       # Electric, Gas, Sanitary
+
+    # Technology
+    "35": "TECHNOLOGY",      # Industrial Machinery (computers)
+    "36": "TECHNOLOGY",      # Electronic Equipment
+    "38": "TECHNOLOGY",      # Instruments
+    "73": "TECHNOLOGY",      # Business Services (software)
+
+    # Healthcare
+    "28": "HEALTHCARE",      # Chemicals (pharma)
+    "80": "HEALTHCARE",      # Health Services
+
+    # Consumer
+    "52": "RETAIL",          # Building Materials Retail
+    "53": "RETAIL",          # General Merchandise
+    "54": "RETAIL",          # Food Stores
+    "56": "RETAIL",          # Apparel
+    "57": "RETAIL",          # Furniture
+    "58": "RETAIL",          # Eating Places
+    "59": "RETAIL",          # Misc Retail (incl. e-commerce)
+
+    # Industrials
+    "37": "INDUSTRIALS",     # Transportation Equipment
+    "40": "TRANSPORTATION",  # Railroad
+    "42": "TRANSPORTATION",  # Trucking
+    "44": "TRANSPORTATION",  # Water Transport
+    "45": "TRANSPORTATION",  # Air Transport
+
+    # Materials
+    "14": "MATERIALS",       # Mining (non-metallic)
+    "24": "MATERIALS",       # Lumber
+    "26": "MATERIALS",       # Paper
+    "32": "MATERIALS",       # Stone, Clay, Glass
+    "33": "MATERIALS",       # Primary Metals
+}
+
+
+def get_sector_from_sic(sic_code: str) -> str:
+    """Get sector classification from SIC code.
+
+    Checks 4-digit specific codes first (e.g., 6798 for REITs),
+    then falls back to 2-digit prefix mapping.
+    """
+    if not sic_code:
+        return "GENERAL"
+    sic_str = str(sic_code)
+
+    # Check 4-digit specific codes first
+    if sic_str in SIC_SPECIFIC_MAP:
+        return SIC_SPECIFIC_MAP[sic_str]
+
+    # Fall back to 2-digit prefix
+    prefix = sic_str[:2]
+    return SIC_SECTOR_MAP.get(prefix, "GENERAL")
+
+
+# =============================================================================
+# INDUSTRY-SPECIFIC XBRL CONCEPTS
+# =============================================================================
+
+# Insurance (SIC 63xx, 64xx)
+INSURANCE_CONCEPTS = {
+    "premiums_earned": ["PremiumsEarnedNet", "PremiumsWrittenNet", "PremiumsEarned"],
+    "claims_incurred": ["PolicyholderBenefitsAndClaimsIncurredNet", "BenefitsLossesAndExpenses",
+                        "PolicyholderBenefitsAndClaimsIncurredGross"],
+    "underwriting_income": ["UnderwritingIncomeLoss", "UnderwritingResultsPropertyCasualtyInsurance"],
+    "investment_income": ["NetInvestmentIncome", "InvestmentIncomeNet", "InvestmentIncomeInterestAndDividend"],
+    "loss_ratio": ["LossRatio", "InsuranceLossRatio"],
+    "policy_acquisition_costs": ["PolicyAcquisitionCosts", "DeferredPolicyAcquisitionCosts"],
+}
+
+# Banks (SIC 60xx, 61xx)
+BANK_CONCEPTS = {
+    "net_interest_income": ["InterestIncomeExpenseNet", "NetInterestIncome",
+                            "InterestIncomeExpenseAfterProvisionForLoanLoss"],
+    "provision_credit_losses": ["ProvisionForLoanLeaseAndOtherLosses", "ProvisionForCreditLosses",
+                                "ProvisionForLoanAndLeaseLosses"],
+    "noninterest_income": ["NoninterestIncome"],
+    "noninterest_expense": ["NoninterestExpense"],
+    "net_loans": ["LoansAndLeasesReceivableNetReportedAmount", "LoansReceivableNet",
+                  "LoansAndLeasesReceivableNetOfDeferredIncome"],
+    "deposits": ["Deposits", "DepositsDomestic"],
+    "tier1_capital_ratio": ["TierOneRiskBasedCapitalRatio", "CommonEquityTier1CapitalRatio"],
+    "net_charge_offs": ["AllowanceForLoanAndLeaseLossesWriteoffsNet", "ChargeOffsNet"],
+}
+
+# REITs (SIC 65xx, 67xx)
+REIT_CONCEPTS = {
+    "rental_revenue": ["OperatingLeaseLeaseIncome", "RentalRevenue", "RevenueFromContractWithCustomerExcludingAssessedTax"],
+    "noi": ["NetOperatingIncome", "OperatingIncomeLoss"],
+    "ffo": ["FundsFromOperations", "FundsFromOperationsPerShare"],
+    "property_operating_expenses": ["CostOfPropertyRepairsAndMaintenance", "RealEstateTaxExpense"],
+    "occupancy_rate": ["OccupancyRate"],
+    "same_store_noi": ["SameStoreNetOperatingIncome"],
+}
+
+# Energy - Oil & Gas (SIC 13xx, 29xx)
+ENERGY_OG_CONCEPTS = {
+    "oil_gas_revenue": ["RevenueFromContractWithCustomerExcludingAssessedTax", "Revenues",
+                        "OilAndGasRevenue", "SalesRevenueNet"],
+    "production_expense": ["ProductionCosts", "LeaseOperatingExpense", "OilAndGasProductionExpense"],
+    "depletion": ["DepletionOfOilAndGasProperties", "DepreciationDepletionAndAmortization"],
+    "proved_reserves": ["ProvedDevelopedAndUndevelopedReserves", "ProvedReservesOil", "ProvedReservesGas"],
+    "exploration_expense": ["ExplorationExpense", "ExplorationCosts"],
+    "impairment": ["ImpairmentOfOilAndGasProperties", "AssetImpairmentCharges"],
+}
+
+# Utilities (SIC 49xx)
+UTILITY_CONCEPTS = {
+    "electric_revenue": ["ElectricUtilityRevenue", "RegulatedElectricRevenue", "ElectricDomesticRevenue"],
+    "gas_revenue": ["GasUtilityRevenue", "RegulatedGasRevenue", "GasDomesticRevenue"],
+    "fuel_cost": ["FuelCosts", "CostOfFuel", "FuelExpense"],
+    "purchased_power_cost": ["CostOfPurchasedPower", "PurchasedPowerCost"],
+    "regulatory_assets": ["RegulatoryAssets"],
+    "regulatory_liabilities": ["RegulatoryLiabilities"],
+    "rate_base": ["UtilityPlantNet", "ElectricUtilityPlantNet"],
+}
+
+# Technology (SIC 35xx, 36xx, 38xx, 73xx)
+TECHNOLOGY_CONCEPTS = {
+    "rd_expense": ["ResearchAndDevelopmentExpense", "ResearchAndDevelopmentExpenseExcludingAcquiredInProcessCost"],
+    "deferred_revenue": ["DeferredRevenue", "ContractWithCustomerLiability", "DeferredRevenueNoncurrent"],
+    "subscription_revenue": ["SubscriptionRevenue", "SaaSRevenue", "RecurringRevenue"],
+    "cost_of_revenue": ["CostOfRevenue", "CostOfGoodsAndServicesSold", "CostOfServices"],
+    "stock_compensation": ["ShareBasedCompensation", "AllocatedShareBasedCompensationExpense"],
+    "intangible_assets": ["IntangibleAssetsNetExcludingGoodwill", "FiniteLivedIntangibleAssetsNet"],
+    "goodwill": ["Goodwill"],
+    "acquired_ip": ["BusinessCombinationRecognizedIdentifiableAssetsAcquiredAndLiabilitiesAssumedIntangibleAssetsOtherThanGoodwill"],
+}
+
+# Healthcare / Pharmaceuticals (SIC 28xx, 80xx)
+HEALTHCARE_CONCEPTS = {
+    "rd_expense": ["ResearchAndDevelopmentExpense", "ResearchAndDevelopmentExpenseExcludingAcquiredInProcessCost"],
+    "cost_of_revenue": ["CostOfRevenue", "CostOfGoodsAndServicesSold"],
+    "selling_general_admin": ["SellingGeneralAndAdministrativeExpense", "GeneralAndAdministrativeExpense"],
+    "acquired_iprd": ["ResearchAndDevelopmentInProcess", "AcquiredInProcessResearchAndDevelopment"],
+    "milestone_payments": ["CollaborativeArrangementMilestonePayments", "LicenseAndCollaborationRevenue"],
+    "inventory": ["InventoryNet", "InventoryFinishedGoodsNetOfReserves"],
+    "product_revenue": ["RevenueFromContractWithCustomerExcludingAssessedTax", "ProductSalesRevenue"],
+    "license_revenue": ["LicenseRevenue", "RoyaltyRevenue", "LicenseAndServicesRevenue"],
+}
+
+# Retail (SIC 52xx-59xx)
+RETAIL_CONCEPTS = {
+    "cost_of_goods_sold": ["CostOfGoodsSold", "CostOfGoodsAndServicesSold", "CostOfRevenue"],
+    "inventory": ["InventoryNet", "RetailRelatedInventoryMerchandise"],
+    "selling_general_admin": ["SellingGeneralAndAdministrativeExpense"],
+    "store_count": ["NumberOfStores", "NumberOfRestaurants"],
+    "depreciation": ["DepreciationAndAmortization", "Depreciation"],
+    "lease_expense": ["OperatingLeaseExpense", "OperatingLeaseCost", "LeaseAndRentalExpense"],
+    "same_store_sales": ["SameStoreSales", "ComparableStoreSalesGrowth"],
+    "ecommerce_revenue": ["OnlineRevenue", "DigitalRevenue", "ECommerceRevenue"],
+}
+
+# Financials - Non-Bank (SIC 62xx, 67xx - Securities, Asset Management)
+FINANCIALS_CONCEPTS = {
+    "advisory_fees": ["InvestmentAdvisoryFees", "AssetManagementFees", "AdvisoryFees"],
+    "assets_under_management": ["AssetsUnderManagement", "ClientAssetsUnderManagement"],
+    "trading_revenue": ["PrincipalTransactionsRevenue", "TradingRevenue", "GainLossOnInvestments"],
+    "commission_revenue": ["CommissionsAndFees", "BrokerageCommissionsRevenue"],
+    "compensation_expense": ["LaborAndRelatedExpense", "CompensationAndBenefitsExpense", "EmployeeBenefitsAndShareBasedCompensation"],
+    "investment_income": ["InvestmentIncomeNet", "NetInvestmentIncome"],
+    "performance_fees": ["IncentiveFeeRevenue", "PerformanceBasedFees"],
+    "fund_expenses": ["FundExpenses", "InvestmentCompanyGeneralPartnerAdvisoryService"],
+}
+
+# Industrials / Manufacturing (SIC 37xx)
+INDUSTRIALS_CONCEPTS = {
+    "cost_of_goods_sold": ["CostOfGoodsSold", "CostOfGoodsAndServicesSold"],
+    "inventory": ["InventoryNet", "InventoryRawMaterialsAndSupplies", "InventoryWorkInProcess", "InventoryFinishedGoods"],
+    "depreciation": ["DepreciationAndAmortization", "Depreciation"],
+    "backlog": ["Backlog", "UnfilledOrders", "OrderBacklog"],
+    "capital_expenditure": ["PaymentsToAcquirePropertyPlantAndEquipment", "CapitalExpendituresIncurredButNotYetPaid"],
+    "property_plant_equipment": ["PropertyPlantAndEquipmentNet", "PropertyPlantAndEquipmentGross"],
+    "pension_expense": ["DefinedBenefitPlanNetPeriodicBenefitCost", "PensionAndOtherPostretirementBenefitExpense"],
+    "warranty_expense": ["ProductWarrantyExpense", "StandardProductWarrantyAccrual"],
+}
+
+# Transportation (SIC 40xx-45xx)
+TRANSPORTATION_CONCEPTS = {
+    "operating_revenue": ["OperatingRevenue", "RevenueFromContractWithCustomerExcludingAssessedTax"],
+    "fuel_expense": ["AircraftFuelExpense", "FuelCosts", "FuelExpense"],
+    "labor_expense": ["SalariesWagesAndBenefits", "LaborAndRelatedExpense"],
+    "depreciation": ["DepreciationAndAmortization", "Depreciation"],
+    "maintenance_expense": ["AircraftMaintenanceMaterialsAndRepairs", "MaintenanceAndRepairsExpense"],
+    "revenue_passenger_miles": ["RevenuePassengerMiles", "PassengerRevenueMiles"],
+    "available_seat_miles": ["AvailableSeatMiles", "AvailableSeatMilesASMs"],
+    "load_factor": ["PassengerLoadFactor", "LoadFactor"],
+    "fleet_size": ["NumberOfAircraft", "FleetSize"],
+}
+
+# Materials (SIC 14xx, 24xx, 26xx, 32xx, 33xx)
+MATERIALS_CONCEPTS = {
+    "cost_of_goods_sold": ["CostOfGoodsSold", "CostOfGoodsAndServicesSold"],
+    "inventory": ["InventoryNet", "InventoryRawMaterialsAndSupplies"],
+    "depreciation": ["DepreciationDepletionAndAmortization", "DepreciationAndAmortization"],
+    "energy_costs": ["UtilitiesExpense", "EnergyCosts", "NaturalGasPurchases"],
+    "environmental_liabilities": ["AccruedEnvironmentalLossContingencies", "EnvironmentalLossContingencyStatementOfFinancialPositionExtensibleListNotDisclosed"],
+    "property_plant_equipment": ["PropertyPlantAndEquipmentNet"],
+    "capital_expenditure": ["PaymentsToAcquirePropertyPlantAndEquipment"],
+    "raw_materials": ["InventoryRawMaterialsAndSupplies", "RawMaterials"],
+}
+
+# Mining (SIC 10xx, 12xx)
+MINING_CONCEPTS = {
+    "mining_revenue": ["RevenueFromContractWithCustomerExcludingAssessedTax", "MiningRevenue", "Revenues"],
+    "cost_of_production": ["CostOfGoodsSold", "ProductionCosts", "MiningCosts"],
+    "depletion": ["DepletionOfMinesAndMineralDeposits", "DepreciationDepletionAndAmortization"],
+    "exploration_expense": ["ExplorationExpense", "MineralExplorationCosts", "ExplorationCosts"],
+    "reclamation_liabilities": ["AssetRetirementObligation", "MineReclamationAndClosingLiability"],
+    "mineral_reserves": ["ProvedAndProbableMineralReserves", "MineralReserves"],
+    "depreciation": ["DepreciationAndAmortization"],
+    "royalty_expense": ["RoyaltyExpense", "MiningRoyalties"],
+}
+
+# Map sector to concept dictionary
+INDUSTRY_CONCEPTS = {
+    "INSURANCE": INSURANCE_CONCEPTS,
+    "BANKS": BANK_CONCEPTS,
+    "REAL_ESTATE": REIT_CONCEPTS,
+    "OIL_GAS": ENERGY_OG_CONCEPTS,
+    "UTILITIES": UTILITY_CONCEPTS,
+    "TECHNOLOGY": TECHNOLOGY_CONCEPTS,
+    "HEALTHCARE": HEALTHCARE_CONCEPTS,
+    "RETAIL": RETAIL_CONCEPTS,
+    "FINANCIALS": FINANCIALS_CONCEPTS,
+    "INDUSTRIALS": INDUSTRIALS_CONCEPTS,
+    "TRANSPORTATION": TRANSPORTATION_CONCEPTS,
+    "MATERIALS": MATERIALS_CONCEPTS,
+    "MINING": MINING_CONCEPTS,
+}

mcp-servers/fundamentals-basket/models/schemas.py

@@ -76,15 +76,123 @@ class ParsedFinancials:
     source: str = "Unknown"
     as_of: str = field(default_factory=lambda: datetime.now().strftime("%Y-%m-%d"))
 
+    # Industry classification
+    sector: str = "GENERAL"
+    sic_code: str = ""
+
+    # Insurance-specific metrics (SIC 63xx, 64xx)
+    premiums_earned: Optional[TemporalMetric] = None
+    claims_incurred: Optional[TemporalMetric] = None
+    underwriting_income: Optional[TemporalMetric] = None
+    investment_income: Optional[TemporalMetric] = None
+    policy_acquisition_costs: Optional[TemporalMetric] = None
+
+    # Bank-specific metrics (SIC 60xx, 61xx)
+    net_interest_income: Optional[TemporalMetric] = None
+    provision_credit_losses: Optional[TemporalMetric] = None
+    noninterest_income: Optional[TemporalMetric] = None
+    noninterest_expense: Optional[TemporalMetric] = None
+    net_loans: Optional[TemporalMetric] = None
+    deposits: Optional[TemporalMetric] = None
+    tier1_capital_ratio: Optional[TemporalMetric] = None
+
+    # REIT-specific metrics (SIC 65xx, 67xx)
+    rental_revenue: Optional[TemporalMetric] = None
+    noi: Optional[TemporalMetric] = None
+    ffo: Optional[TemporalMetric] = None
+    property_operating_expenses: Optional[TemporalMetric] = None
+
+    # Energy/Oil & Gas-specific metrics (SIC 13xx, 29xx)
+    oil_gas_revenue: Optional[TemporalMetric] = None
+    production_expense: Optional[TemporalMetric] = None
+    depletion: Optional[TemporalMetric] = None
+    exploration_expense: Optional[TemporalMetric] = None
+    impairment: Optional[TemporalMetric] = None
+
+    # Utility-specific metrics (SIC 49xx)
+    electric_revenue: Optional[TemporalMetric] = None
+    gas_revenue: Optional[TemporalMetric] = None
+    fuel_cost: Optional[TemporalMetric] = None
+    regulatory_assets: Optional[TemporalMetric] = None
+    rate_base: Optional[TemporalMetric] = None
+
+    # Technology-specific metrics (SIC 35xx, 36xx, 38xx, 73xx)
+    rd_expense: Optional[TemporalMetric] = None
+    deferred_revenue: Optional[TemporalMetric] = None
+    subscription_revenue: Optional[TemporalMetric] = None
+    cost_of_revenue: Optional[TemporalMetric] = None
+    stock_compensation: Optional[TemporalMetric] = None
+    intangible_assets: Optional[TemporalMetric] = None
+    goodwill: Optional[TemporalMetric] = None
+    acquired_ip: Optional[TemporalMetric] = None
+
+    # Healthcare-specific metrics (SIC 28xx, 80xx)
+    selling_general_admin: Optional[TemporalMetric] = None
+    acquired_iprd: Optional[TemporalMetric] = None
+    milestone_payments: Optional[TemporalMetric] = None
+    inventory: Optional[TemporalMetric] = None
+    product_revenue: Optional[TemporalMetric] = None
+    license_revenue: Optional[TemporalMetric] = None
+
+    # Retail-specific metrics (SIC 52xx-59xx)
+    cost_of_goods_sold: Optional[TemporalMetric] = None
+    store_count: Optional[TemporalMetric] = None
+    depreciation: Optional[TemporalMetric] = None
+    lease_expense: Optional[TemporalMetric] = None
+    same_store_sales: Optional[TemporalMetric] = None
+    ecommerce_revenue: Optional[TemporalMetric] = None
+
+    # Financials-specific metrics (SIC 62xx, 67xx - non-bank)
+    advisory_fees: Optional[TemporalMetric] = None
+    assets_under_management: Optional[TemporalMetric] = None
+    trading_revenue: Optional[TemporalMetric] = None
+    commission_revenue: Optional[TemporalMetric] = None
+    compensation_expense: Optional[TemporalMetric] = None
+    performance_fees: Optional[TemporalMetric] = None
+    fund_expenses: Optional[TemporalMetric] = None
+
+    # Industrials-specific metrics (SIC 37xx)
+    backlog: Optional[TemporalMetric] = None
+    capital_expenditure: Optional[TemporalMetric] = None
+    property_plant_equipment: Optional[TemporalMetric] = None
+    pension_expense: Optional[TemporalMetric] = None
+    warranty_expense: Optional[TemporalMetric] = None
+
+    # Transportation-specific metrics (SIC 40xx-45xx)
+    operating_revenue: Optional[TemporalMetric] = None
+    fuel_expense: Optional[TemporalMetric] = None
+    labor_expense: Optional[TemporalMetric] = None
+    maintenance_expense: Optional[TemporalMetric] = None
+    revenue_passenger_miles: Optional[TemporalMetric] = None
+    available_seat_miles: Optional[TemporalMetric] = None
+    load_factor: Optional[TemporalMetric] = None
+    fleet_size: Optional[TemporalMetric] = None
+
+    # Materials-specific metrics (SIC 14xx, 24xx, 26xx, 32xx, 33xx)
+    energy_costs: Optional[TemporalMetric] = None
+    environmental_liabilities: Optional[TemporalMetric] = None
+    raw_materials: Optional[TemporalMetric] = None
+
+    # Mining-specific metrics (SIC 10xx, 12xx)
+    mining_revenue: Optional[TemporalMetric] = None
+    cost_of_production: Optional[TemporalMetric] = None
+    reclamation_liabilities: Optional[TemporalMetric] = None
+    mineral_reserves: Optional[TemporalMetric] = None
+    royalty_expense: Optional[TemporalMetric] = None
+
     def to_dict(self) -> dict:
         """Convert to dictionary for JSON serialization."""
         result = {
             "ticker": self.ticker,
             "source": self.source,
             "as_of": self.as_of,
+            "sector": self.sector,
         }
 
-        # Add temporal metrics
+        if self.sic_code:
+            result["sic_code"] = self.sic_code
+
+        # Add temporal metrics - universal fields
         for field_name in [
             "revenue", "net_income", "gross_profit", "operating_income",
             "gross_margin_pct", "operating_margin_pct", "net_margin_pct",
@@ -94,6 +202,52 @@ class ParsedFinancials:
             if value:
                 result[field_name] = value.to_dict() if isinstance(value, TemporalMetric) else value
 
+        # Add industry-specific fields (only if present)
+        industry_fields = [
+            # Insurance
+            "premiums_earned", "claims_incurred", "underwriting_income",
+            "investment_income", "policy_acquisition_costs",
+            # Banks
+            "net_interest_income", "provision_credit_losses", "noninterest_income",
+            "noninterest_expense", "net_loans", "deposits", "tier1_capital_ratio",
+            # REITs
+            "rental_revenue", "noi", "ffo", "property_operating_expenses",
+            # Energy
+            "oil_gas_revenue", "production_expense", "depletion",
+            "exploration_expense", "impairment",
+            # Utilities
+            "electric_revenue", "gas_revenue", "fuel_cost",
+            "regulatory_assets", "rate_base",
+            # Technology
+            "rd_expense", "deferred_revenue", "subscription_revenue", "cost_of_revenue",
+            "stock_compensation", "intangible_assets", "goodwill", "acquired_ip",
+            # Healthcare
+            "selling_general_admin", "acquired_iprd", "milestone_payments",
+            "inventory", "product_revenue", "license_revenue",
+            # Retail
+            "cost_of_goods_sold", "store_count", "depreciation", "lease_expense",
+            "same_store_sales", "ecommerce_revenue",
+            # Financials
+            "advisory_fees", "assets_under_management", "trading_revenue",
+            "commission_revenue", "compensation_expense", "performance_fees", "fund_expenses",
+            # Industrials
+            "backlog", "capital_expenditure", "property_plant_equipment",
+            "pension_expense", "warranty_expense",
+            # Transportation
+            "operating_revenue", "fuel_expense", "labor_expense", "maintenance_expense",
+            "revenue_passenger_miles", "available_seat_miles", "load_factor", "fleet_size",
+            # Materials
+            "energy_costs", "environmental_liabilities", "raw_materials",
+            # Mining
+            "mining_revenue", "cost_of_production", "reclamation_liabilities",
+            "mineral_reserves", "royalty_expense",
+        ]
+
+        for field_name in industry_fields:
+            value = getattr(self, field_name)
+            if value:
+                result[field_name] = value.to_dict() if isinstance(value, TemporalMetric) else value
+
         return result
 
 

mcp-servers/fundamentals-basket/services/orchestrator.py

@@ -13,7 +13,7 @@ import logging
 from datetime import datetime
 from typing import Optional, Dict, Any
 
-from config import TOOL_TIMEOUT
+from config import TOOL_TIMEOUT, get_sector_from_sic
 from models.schemas import (
     TemporalMetric,
     ParsedFinancials,
@@ -97,6 +97,7 @@ class OrchestratorService:
                 "sic_description": submissions.get("sicDescription"),
                 "state_of_incorporation": submissions.get("stateOfIncorporation"),
                 "fiscal_year_end": submissions.get("fiscalYearEnd"),
+                "business_address": submissions.get("addresses", {}).get("business", {}),
                 "source": "SEC EDGAR",
             }
 
@@ -141,8 +142,14 @@ class OrchestratorService:
             if not facts:
                 return await self._get_yfinance_financials(ticker)
 
-            # Parse financials
-            financials = self.parser.parse_financials(facts, ticker)
+            # Get company info for SIC-based sector detection
+            company_info = await self.get_company_info(ticker)
+            sic_code = company_info.get("sic", "")
+            sector = get_sector_from_sic(sic_code)
+            logger.info(f"Detected sector for {ticker}: {sector} (SIC: {sic_code})")
+
+            # Parse financials with industry-specific metrics
+            financials = self.parser.parse_financials(facts, ticker, sector=sector, sic_code=sic_code)
             return financials.to_dict()
 
         except (APITimeoutError, CircuitOpenError) as e:
@@ -261,17 +268,20 @@ class OrchestratorService:
         if not facts:
             raise ValueError("No company facts available")
 
-        # Parse all metrics
-        financials = self.parser.parse_financials(facts, ticker)
+        # Get company info for SIC-based sector detection
+        company_info = await self.get_company_info(ticker)
+        sic_code = company_info.get("sic", "")
+        sector = get_sector_from_sic(sic_code)
+        logger.info(f"SEC Basket - Detected sector for {ticker}: {sector} (SIC: {sic_code})")
+
+        # Parse all metrics with industry-specific extraction
+        financials = self.parser.parse_financials(facts, ticker, sector=sector, sic_code=sic_code)
         debt = self.parser.parse_debt_metrics(facts, ticker)
         cash_flow = self.parser.parse_cash_flow(facts, ticker)
 
         # Build SWOT
         swot = self.parser.build_swot_summary(financials, debt, cash_flow)
 
-        # Get company info
-        company_info = await self.get_company_info(ticker)
-
         # Build basket
         basket = FinancialsBasket(
             ticker=ticker,
@@ -396,16 +406,20 @@ class OrchestratorService:
                     "data": yahoo_result.get("data"),
                 }
 
+        # Get company info for response (includes business_address)
+        company_info = await self.get_company_info(ticker)
+
         return {
             "group": "source_comparison",
             "ticker": ticker,
+            "company": company_info,
             "sources": sources,
             "source": "fundamentals-basket",
             "as_of": datetime.now().strftime("%Y-%m-%d"),
         }
 
     async def _get_sec_data_safe(self, ticker: str) -> Dict[str, Any]:
-        """Get SEC data with error handling. Returns 6 universal metrics only."""
+        """Get SEC data with error handling. Returns universal + industry-specific metrics."""
         try:
             cik = await self._get_cik_with_cache(ticker)
             if not cik:
@@ -415,7 +429,12 @@ class OrchestratorService:
             if not facts:
                 return {"error": "No facts available", "source": "SEC EDGAR"}
 
-            financials = self.parser.parse_financials(facts, ticker)
+            # Get company info for SIC-based sector detection
+            company_info = await self.get_company_info(ticker)
+            sic_code = company_info.get("sic", "")
+            sector = get_sector_from_sic(sic_code)
+
+            financials = self.parser.parse_financials(facts, ticker, sector=sector, sic_code=sic_code)
 
             # Helper to convert TemporalMetric to dict (include all temporal fields)
             def to_metric_dict(tm):
@@ -429,18 +448,112 @@ class OrchestratorService:
                     "form": tm.form,
                 }
 
-            # Only 6 universal metrics (works across all industries)
+            # Universal metrics (works across all industries)
+            data = {
+                "revenue": to_metric_dict(financials.revenue),
+                "net_income": to_metric_dict(financials.net_income),
+                "net_margin_pct": to_metric_dict(financials.net_margin_pct),
+                "total_assets": to_metric_dict(financials.total_assets),
+                "total_liabilities": to_metric_dict(financials.total_liabilities),
+                "stockholders_equity": to_metric_dict(financials.stockholders_equity),
+            }
+
+            # Add industry-specific metrics if available
+            if sector == "INSURANCE":
+                data.update({
+                    "premiums_earned": to_metric_dict(financials.premiums_earned),
+                    "claims_incurred": to_metric_dict(financials.claims_incurred),
+                    "underwriting_income": to_metric_dict(financials.underwriting_income),
+                    "investment_income": to_metric_dict(financials.investment_income),
+                })
+            elif sector == "BANKS":
+                data.update({
+                    "net_interest_income": to_metric_dict(financials.net_interest_income),
+                    "provision_credit_losses": to_metric_dict(financials.provision_credit_losses),
+                    "noninterest_income": to_metric_dict(financials.noninterest_income),
+                    "deposits": to_metric_dict(financials.deposits),
+                })
+            elif sector == "REAL_ESTATE":
+                data.update({
+                    "rental_revenue": to_metric_dict(financials.rental_revenue),
+                    "noi": to_metric_dict(financials.noi),
+                    "ffo": to_metric_dict(financials.ffo),
+                })
+            elif sector == "OIL_GAS":
+                data.update({
+                    "oil_gas_revenue": to_metric_dict(financials.oil_gas_revenue),
+                    "production_expense": to_metric_dict(financials.production_expense),
+                    "depletion": to_metric_dict(financials.depletion),
+                })
+            elif sector == "UTILITIES":
+                data.update({
+                    "electric_revenue": to_metric_dict(financials.electric_revenue),
+                    "gas_revenue": to_metric_dict(financials.gas_revenue),
+                    "fuel_cost": to_metric_dict(financials.fuel_cost),
+                })
+            elif sector == "TECHNOLOGY":
+                data.update({
+                    "rd_expense": to_metric_dict(financials.rd_expense),
+                    "deferred_revenue": to_metric_dict(financials.deferred_revenue),
+                    "cost_of_revenue": to_metric_dict(financials.cost_of_revenue),
+                    "goodwill": to_metric_dict(financials.goodwill),
+                })
+            elif sector == "HEALTHCARE":
+                data.update({
+                    "rd_expense": to_metric_dict(financials.rd_expense),
+                    "cost_of_revenue": to_metric_dict(financials.cost_of_revenue),
+                    "inventory": to_metric_dict(financials.inventory),
+                    "selling_general_admin": to_metric_dict(financials.selling_general_admin),
+                })
+            elif sector == "RETAIL":
+                data.update({
+                    "cost_of_goods_sold": to_metric_dict(financials.cost_of_goods_sold),
+                    "inventory": to_metric_dict(financials.inventory),
+                    "selling_general_admin": to_metric_dict(financials.selling_general_admin),
+                    "depreciation": to_metric_dict(financials.depreciation),
+                })
+            elif sector == "FINANCIALS":
+                data.update({
+                    "advisory_fees": to_metric_dict(financials.advisory_fees),
+                    "trading_revenue": to_metric_dict(financials.trading_revenue),
+                    "compensation_expense": to_metric_dict(financials.compensation_expense),
+                    "investment_income": to_metric_dict(financials.investment_income),
+                })
+            elif sector == "INDUSTRIALS":
+                data.update({
+                    "cost_of_goods_sold": to_metric_dict(financials.cost_of_goods_sold),
+                    "inventory": to_metric_dict(financials.inventory),
+                    "backlog": to_metric_dict(financials.backlog),
+                    "capital_expenditure": to_metric_dict(financials.capital_expenditure),
+                })
+            elif sector == "TRANSPORTATION":
+                data.update({
+                    "operating_revenue": to_metric_dict(financials.operating_revenue),
+                    "fuel_expense": to_metric_dict(financials.fuel_expense),
+                    "labor_expense": to_metric_dict(financials.labor_expense),
+                    "depreciation": to_metric_dict(financials.depreciation),
+                })
+            elif sector == "MATERIALS":
+                data.update({
+                    "cost_of_goods_sold": to_metric_dict(financials.cost_of_goods_sold),
+                    "inventory": to_metric_dict(financials.inventory),
+                    "depreciation": to_metric_dict(financials.depreciation),
+                    "capital_expenditure": to_metric_dict(financials.capital_expenditure),
+                })
+            elif sector == "MINING":
+                data.update({
+                    "mining_revenue": to_metric_dict(financials.mining_revenue),
+                    "cost_of_production": to_metric_dict(financials.cost_of_production),
+                    "depletion": to_metric_dict(financials.depletion),
+                    "exploration_expense": to_metric_dict(financials.exploration_expense),
+                })
+
             return {
                 "source": "SEC EDGAR XBRL",
                 "as_of": datetime.now().strftime("%Y-%m-%d"),
-                "data": {
-                    "revenue": to_metric_dict(financials.revenue),
-                    "net_income": to_metric_dict(financials.net_income),
-                    "net_margin_pct": to_metric_dict(financials.net_margin_pct),
-                    "total_assets": to_metric_dict(financials.total_assets),
-                    "total_liabilities": to_metric_dict(financials.total_liabilities),
-                    "stockholders_equity": to_metric_dict(financials.stockholders_equity),
-                },
+                "sector": sector,
+                "sic_code": sic_code,
+                "data": data,
             }
 
         except Exception as e:

mcp-servers/fundamentals-basket/services/parser.py

@@ -25,6 +25,21 @@ from config import (
     DEBT_TO_EQUITY_ELEVATED,
     DEBT_TO_EQUITY_LOW,
     RD_HIGH_INVESTMENT,
+    # Industry-specific concepts
+    INDUSTRY_CONCEPTS,
+    INSURANCE_CONCEPTS,
+    BANK_CONCEPTS,
+    REIT_CONCEPTS,
+    ENERGY_OG_CONCEPTS,
+    UTILITY_CONCEPTS,
+    TECHNOLOGY_CONCEPTS,
+    HEALTHCARE_CONCEPTS,
+    RETAIL_CONCEPTS,
+    FINANCIALS_CONCEPTS,
+    INDUSTRIALS_CONCEPTS,
+    TRANSPORTATION_CONCEPTS,
+    MATERIALS_CONCEPTS,
+    MINING_CONCEPTS,
 )
 from models.schemas import (
     TemporalMetric,
@@ -322,7 +337,9 @@ class ParserService:
     def parse_financials(
         self,
         facts: Dict[str, Any],
-        ticker: str
+        ticker: str,
+        sector: str = "GENERAL",
+        sic_code: str = ""
     ) -> ParsedFinancials:
         """
         Parse financial metrics from XBRL facts.
@@ -330,11 +347,13 @@ class ParserService:
         Args:
             facts: Company facts dict from SEC EDGAR
             ticker: Stock ticker symbol
+            sector: Industry sector (INSURANCE, BANKS, REAL_ESTATE, OIL_GAS, UTILITIES, GENERAL)
+            sic_code: SIC code from SEC EDGAR
 
         Returns:
-            ParsedFinancials with all metrics
+            ParsedFinancials with all metrics (universal + industry-specific)
         """
-        # Extract core metrics
+        # Extract core metrics (universal)
         revenue = self.get_latest_value(facts, REVENUE_CONCEPTS)
         net_income = self.get_latest_value(facts, NET_INCOME_CONCEPTS)
         gross_profit = self.get_latest_value(facts, GROSS_PROFIT_CONCEPTS)
@@ -373,6 +392,50 @@ class ParserService:
         if revenue_growth_val is not None:
             revenue_growth_3yr = self.create_temporal_metric(revenue_growth_val, revenue)
 
+        # Initialize industry-specific fields
+        industry_metrics = {}
+
+        # Extract industry-specific metrics based on sector
+        if sector == "INSURANCE":
+            industry_metrics = self._extract_insurance_metrics(facts)
+            logger.info(f"Extracted insurance metrics for {ticker}: {list(industry_metrics.keys())}")
+        elif sector == "BANKS":
+            industry_metrics = self._extract_bank_metrics(facts)
+            logger.info(f"Extracted bank metrics for {ticker}: {list(industry_metrics.keys())}")
+        elif sector == "REAL_ESTATE":
+            industry_metrics = self._extract_reit_metrics(facts)
+            logger.info(f"Extracted REIT metrics for {ticker}: {list(industry_metrics.keys())}")
+        elif sector == "OIL_GAS":
+            industry_metrics = self._extract_energy_metrics(facts)
+            logger.info(f"Extracted energy metrics for {ticker}: {list(industry_metrics.keys())}")
+        elif sector == "UTILITIES":
+            industry_metrics = self._extract_utility_metrics(facts)
+            logger.info(f"Extracted utility metrics for {ticker}: {list(industry_metrics.keys())}")
+        elif sector == "TECHNOLOGY":
+            industry_metrics = self._extract_technology_metrics(facts)
+            logger.info(f"Extracted technology metrics for {ticker}: {list(industry_metrics.keys())}")
+        elif sector == "HEALTHCARE":
+            industry_metrics = self._extract_healthcare_metrics(facts)
+            logger.info(f"Extracted healthcare metrics for {ticker}: {list(industry_metrics.keys())}")
+        elif sector == "RETAIL":
+            industry_metrics = self._extract_retail_metrics(facts)
+            logger.info(f"Extracted retail metrics for {ticker}: {list(industry_metrics.keys())}")
+        elif sector == "FINANCIALS":
+            industry_metrics = self._extract_financials_metrics(facts)
+            logger.info(f"Extracted financials metrics for {ticker}: {list(industry_metrics.keys())}")
+        elif sector == "INDUSTRIALS":
+            industry_metrics = self._extract_industrials_metrics(facts)
+            logger.info(f"Extracted industrials metrics for {ticker}: {list(industry_metrics.keys())}")
+        elif sector == "TRANSPORTATION":
+            industry_metrics = self._extract_transportation_metrics(facts)
+            logger.info(f"Extracted transportation metrics for {ticker}: {list(industry_metrics.keys())}")
+        elif sector == "MATERIALS":
+            industry_metrics = self._extract_materials_metrics(facts)
+            logger.info(f"Extracted materials metrics for {ticker}: {list(industry_metrics.keys())}")
+        elif sector == "MINING":
+            industry_metrics = self._extract_mining_metrics(facts)
+            logger.info(f"Extracted mining metrics for {ticker}: {list(industry_metrics.keys())}")
+
         return ParsedFinancials(
             ticker=ticker.upper(),
             revenue=revenue,
@@ -387,8 +450,171 @@ class ParserService:
             total_liabilities=total_liabilities,
             stockholders_equity=stockholders_equity,
             source="SEC EDGAR XBRL",
+            sector=sector,
+            sic_code=sic_code,
+            **industry_metrics,
         )
 
+    # =========================================================================
+    # INDUSTRY-SPECIFIC EXTRACTION METHODS
+    # =========================================================================
+
+    def _extract_insurance_metrics(self, facts: Dict[str, Any]) -> Dict[str, Optional[TemporalMetric]]:
+        """Extract insurance-specific metrics from XBRL facts."""
+        return {
+            "premiums_earned": self.get_latest_value(facts, INSURANCE_CONCEPTS["premiums_earned"]),
+            "claims_incurred": self.get_latest_value(facts, INSURANCE_CONCEPTS["claims_incurred"]),
+            "underwriting_income": self.get_latest_value(facts, INSURANCE_CONCEPTS["underwriting_income"]),
+            "investment_income": self.get_latest_value(facts, INSURANCE_CONCEPTS["investment_income"]),
+            "policy_acquisition_costs": self.get_latest_value(facts, INSURANCE_CONCEPTS["policy_acquisition_costs"]),
+        }
+
+    def _extract_bank_metrics(self, facts: Dict[str, Any]) -> Dict[str, Optional[TemporalMetric]]:
+        """Extract bank-specific metrics from XBRL facts."""
+        return {
+            "net_interest_income": self.get_latest_value(facts, BANK_CONCEPTS["net_interest_income"]),
+            "provision_credit_losses": self.get_latest_value(facts, BANK_CONCEPTS["provision_credit_losses"]),
+            "noninterest_income": self.get_latest_value(facts, BANK_CONCEPTS["noninterest_income"]),
+            "noninterest_expense": self.get_latest_value(facts, BANK_CONCEPTS["noninterest_expense"]),
+            "net_loans": self.get_latest_value(facts, BANK_CONCEPTS["net_loans"]),
+            "deposits": self.get_latest_value(facts, BANK_CONCEPTS["deposits"]),
+            "tier1_capital_ratio": self.get_latest_value(facts, BANK_CONCEPTS["tier1_capital_ratio"], unit="pure"),
+        }
+
+    def _extract_reit_metrics(self, facts: Dict[str, Any]) -> Dict[str, Optional[TemporalMetric]]:
+        """Extract REIT-specific metrics from XBRL facts."""
+        return {
+            "rental_revenue": self.get_latest_value(facts, REIT_CONCEPTS["rental_revenue"]),
+            "noi": self.get_latest_value(facts, REIT_CONCEPTS["noi"]),
+            "ffo": self.get_latest_value(facts, REIT_CONCEPTS["ffo"]),
+            "property_operating_expenses": self.get_latest_value(facts, REIT_CONCEPTS["property_operating_expenses"]),
+        }
+
+    def _extract_energy_metrics(self, facts: Dict[str, Any]) -> Dict[str, Optional[TemporalMetric]]:
+        """Extract energy/oil & gas-specific metrics from XBRL facts."""
+        return {
+            "oil_gas_revenue": self.get_latest_value(facts, ENERGY_OG_CONCEPTS["oil_gas_revenue"]),
+            "production_expense": self.get_latest_value(facts, ENERGY_OG_CONCEPTS["production_expense"]),
+            "depletion": self.get_latest_value(facts, ENERGY_OG_CONCEPTS["depletion"]),
+            "exploration_expense": self.get_latest_value(facts, ENERGY_OG_CONCEPTS["exploration_expense"]),
+            "impairment": self.get_latest_value(facts, ENERGY_OG_CONCEPTS["impairment"]),
+        }
+
+    def _extract_utility_metrics(self, facts: Dict[str, Any]) -> Dict[str, Optional[TemporalMetric]]:
+        """Extract utility-specific metrics from XBRL facts."""
+        return {
+            "electric_revenue": self.get_latest_value(facts, UTILITY_CONCEPTS["electric_revenue"]),
+            "gas_revenue": self.get_latest_value(facts, UTILITY_CONCEPTS["gas_revenue"]),
+            "fuel_cost": self.get_latest_value(facts, UTILITY_CONCEPTS["fuel_cost"]),
+            "regulatory_assets": self.get_latest_value(facts, UTILITY_CONCEPTS["regulatory_assets"]),
+            "rate_base": self.get_latest_value(facts, UTILITY_CONCEPTS["rate_base"]),
+        }
+
+    def _extract_technology_metrics(self, facts: Dict[str, Any]) -> Dict[str, Optional[TemporalMetric]]:
+        """Extract technology-specific metrics from XBRL facts."""
+        return {
+            "rd_expense": self.get_latest_value(facts, TECHNOLOGY_CONCEPTS["rd_expense"]),
+            "deferred_revenue": self.get_latest_value(facts, TECHNOLOGY_CONCEPTS["deferred_revenue"]),
+            "subscription_revenue": self.get_latest_value(facts, TECHNOLOGY_CONCEPTS["subscription_revenue"]),
+            "cost_of_revenue": self.get_latest_value(facts, TECHNOLOGY_CONCEPTS["cost_of_revenue"]),
+            "stock_compensation": self.get_latest_value(facts, TECHNOLOGY_CONCEPTS["stock_compensation"]),
+            "intangible_assets": self.get_latest_value(facts, TECHNOLOGY_CONCEPTS["intangible_assets"]),
+            "goodwill": self.get_latest_value(facts, TECHNOLOGY_CONCEPTS["goodwill"]),
+            "acquired_ip": self.get_latest_value(facts, TECHNOLOGY_CONCEPTS["acquired_ip"]),
+        }
+
+    def _extract_healthcare_metrics(self, facts: Dict[str, Any]) -> Dict[str, Optional[TemporalMetric]]:
+        """Extract healthcare/pharma-specific metrics from XBRL facts."""
+        return {
+            "rd_expense": self.get_latest_value(facts, HEALTHCARE_CONCEPTS["rd_expense"]),
+            "cost_of_revenue": self.get_latest_value(facts, HEALTHCARE_CONCEPTS["cost_of_revenue"]),
+            "selling_general_admin": self.get_latest_value(facts, HEALTHCARE_CONCEPTS["selling_general_admin"]),
+            "acquired_iprd": self.get_latest_value(facts, HEALTHCARE_CONCEPTS["acquired_iprd"]),
+            "milestone_payments": self.get_latest_value(facts, HEALTHCARE_CONCEPTS["milestone_payments"]),
+            "inventory": self.get_latest_value(facts, HEALTHCARE_CONCEPTS["inventory"]),
+            "product_revenue": self.get_latest_value(facts, HEALTHCARE_CONCEPTS["product_revenue"]),
+            "license_revenue": self.get_latest_value(facts, HEALTHCARE_CONCEPTS["license_revenue"]),
+        }
+
+    def _extract_retail_metrics(self, facts: Dict[str, Any]) -> Dict[str, Optional[TemporalMetric]]:
+        """Extract retail-specific metrics from XBRL facts."""
+        return {
+            "cost_of_goods_sold": self.get_latest_value(facts, RETAIL_CONCEPTS["cost_of_goods_sold"]),
+            "inventory": self.get_latest_value(facts, RETAIL_CONCEPTS["inventory"]),
+            "selling_general_admin": self.get_latest_value(facts, RETAIL_CONCEPTS["selling_general_admin"]),
+            "store_count": self.get_latest_value(facts, RETAIL_CONCEPTS["store_count"], unit="pure"),
+            "depreciation": self.get_latest_value(facts, RETAIL_CONCEPTS["depreciation"]),
+            "lease_expense": self.get_latest_value(facts, RETAIL_CONCEPTS["lease_expense"]),
+            "same_store_sales": self.get_latest_value(facts, RETAIL_CONCEPTS["same_store_sales"], unit="pure"),
+            "ecommerce_revenue": self.get_latest_value(facts, RETAIL_CONCEPTS["ecommerce_revenue"]),
+        }
+
+    def _extract_financials_metrics(self, facts: Dict[str, Any]) -> Dict[str, Optional[TemporalMetric]]:
+        """Extract financials (non-bank) metrics from XBRL facts."""
+        return {
+            "advisory_fees": self.get_latest_value(facts, FINANCIALS_CONCEPTS["advisory_fees"]),
+            "assets_under_management": self.get_latest_value(facts, FINANCIALS_CONCEPTS["assets_under_management"]),
+            "trading_revenue": self.get_latest_value(facts, FINANCIALS_CONCEPTS["trading_revenue"]),
+            "commission_revenue": self.get_latest_value(facts, FINANCIALS_CONCEPTS["commission_revenue"]),
+            "compensation_expense": self.get_latest_value(facts, FINANCIALS_CONCEPTS["compensation_expense"]),
+            "investment_income": self.get_latest_value(facts, FINANCIALS_CONCEPTS["investment_income"]),
+            "performance_fees": self.get_latest_value(facts, FINANCIALS_CONCEPTS["performance_fees"]),
+            "fund_expenses": self.get_latest_value(facts, FINANCIALS_CONCEPTS["fund_expenses"]),
+        }
+
+    def _extract_industrials_metrics(self, facts: Dict[str, Any]) -> Dict[str, Optional[TemporalMetric]]:
+        """Extract industrials/manufacturing metrics from XBRL facts."""
+        return {
+            "cost_of_goods_sold": self.get_latest_value(facts, INDUSTRIALS_CONCEPTS["cost_of_goods_sold"]),
+            "inventory": self.get_latest_value(facts, INDUSTRIALS_CONCEPTS["inventory"]),
+            "depreciation": self.get_latest_value(facts, INDUSTRIALS_CONCEPTS["depreciation"]),
+            "backlog": self.get_latest_value(facts, INDUSTRIALS_CONCEPTS["backlog"]),
+            "capital_expenditure": self.get_latest_value(facts, INDUSTRIALS_CONCEPTS["capital_expenditure"]),
+            "property_plant_equipment": self.get_latest_value(facts, INDUSTRIALS_CONCEPTS["property_plant_equipment"]),
+            "pension_expense": self.get_latest_value(facts, INDUSTRIALS_CONCEPTS["pension_expense"]),
+            "warranty_expense": self.get_latest_value(facts, INDUSTRIALS_CONCEPTS["warranty_expense"]),
+        }
+
+    def _extract_transportation_metrics(self, facts: Dict[str, Any]) -> Dict[str, Optional[TemporalMetric]]:
+        """Extract transportation-specific metrics from XBRL facts."""
+        return {
+            "operating_revenue": self.get_latest_value(facts, TRANSPORTATION_CONCEPTS["operating_revenue"]),
+            "fuel_expense": self.get_latest_value(facts, TRANSPORTATION_CONCEPTS["fuel_expense"]),
+            "labor_expense": self.get_latest_value(facts, TRANSPORTATION_CONCEPTS["labor_expense"]),
+            "depreciation": self.get_latest_value(facts, TRANSPORTATION_CONCEPTS["depreciation"]),
+            "maintenance_expense": self.get_latest_value(facts, TRANSPORTATION_CONCEPTS["maintenance_expense"]),
+            "revenue_passenger_miles": self.get_latest_value(facts, TRANSPORTATION_CONCEPTS["revenue_passenger_miles"], unit="pure"),
+            "available_seat_miles": self.get_latest_value(facts, TRANSPORTATION_CONCEPTS["available_seat_miles"], unit="pure"),
+            "load_factor": self.get_latest_value(facts, TRANSPORTATION_CONCEPTS["load_factor"], unit="pure"),
+            "fleet_size": self.get_latest_value(facts, TRANSPORTATION_CONCEPTS["fleet_size"], unit="pure"),
+        }
+
+    def _extract_materials_metrics(self, facts: Dict[str, Any]) -> Dict[str, Optional[TemporalMetric]]:
+        """Extract materials-specific metrics from XBRL facts."""
+        return {
+            "cost_of_goods_sold": self.get_latest_value(facts, MATERIALS_CONCEPTS["cost_of_goods_sold"]),
+            "inventory": self.get_latest_value(facts, MATERIALS_CONCEPTS["inventory"]),
+            "depreciation": self.get_latest_value(facts, MATERIALS_CONCEPTS["depreciation"]),
+            "energy_costs": self.get_latest_value(facts, MATERIALS_CONCEPTS["energy_costs"]),
+            "environmental_liabilities": self.get_latest_value(facts, MATERIALS_CONCEPTS["environmental_liabilities"]),
+            "property_plant_equipment": self.get_latest_value(facts, MATERIALS_CONCEPTS["property_plant_equipment"]),
+            "capital_expenditure": self.get_latest_value(facts, MATERIALS_CONCEPTS["capital_expenditure"]),
+            "raw_materials": self.get_latest_value(facts, MATERIALS_CONCEPTS["raw_materials"]),
+        }
+
+    def _extract_mining_metrics(self, facts: Dict[str, Any]) -> Dict[str, Optional[TemporalMetric]]:
+        """Extract mining-specific metrics from XBRL facts."""
+        return {
+            "mining_revenue": self.get_latest_value(facts, MINING_CONCEPTS["mining_revenue"]),
+            "cost_of_production": self.get_latest_value(facts, MINING_CONCEPTS["cost_of_production"]),
+            "depletion": self.get_latest_value(facts, MINING_CONCEPTS["depletion"]),
+            "exploration_expense": self.get_latest_value(facts, MINING_CONCEPTS["exploration_expense"]),
+            "reclamation_liabilities": self.get_latest_value(facts, MINING_CONCEPTS["reclamation_liabilities"]),
+            "mineral_reserves": self.get_latest_value(facts, MINING_CONCEPTS["mineral_reserves"], unit="pure"),
+            "depreciation": self.get_latest_value(facts, MINING_CONCEPTS["depreciation"]),
+            "royalty_expense": self.get_latest_value(facts, MINING_CONCEPTS["royalty_expense"]),
+        }
+
     def parse_debt_metrics(
         self,
         facts: Dict[str, Any],

tests/test_mcp_e2e.py

@@ -534,6 +534,35 @@ def generate_report(results: List[MCPTestResult], ticker: str, company_name: str
         warnings = "; ".join(r.warnings) if r.warnings else "-"
         lines.append(f"| {i} | {r.name} | {r.status} | {expected} | {r.item_count} | {r.duration_ms}ms | {errors} | {warnings} |")
 
+    # Company Info (from fundamentals)
+    fund_result = next((r for r in results if r.name == "fundamentals"), None)
+    if fund_result and fund_result.data:
+        company = fund_result.data.get("company", {})
+        if company:
+            lines.extend([
+                "",
+                "---",
+                "",
+                "## Company Info",
+                "",
+                f"| Field | Value |",
+                f"|-------|-------|",
+                f"| Name | {company.get('name', '-')} |",
+                f"| CIK | {company.get('cik', '-')} |",
+                f"| SIC | {company.get('sic', '-')} ({company.get('sic_description', '-')}) |",
+                f"| State | {company.get('state_of_incorporation', '-')} |",
+                f"| Fiscal Year End | {company.get('fiscal_year_end', '-')} |",
+            ])
+            # Business address
+            addr = company.get("business_address", {})
+            if addr:
+                street = addr.get("street1", "")
+                if addr.get("street2"):
+                    street += f", {addr.get('street2')}"
+                city_state_zip = f"{addr.get('city', '')}, {addr.get('stateOrCountry', '')} {addr.get('zipCode', '')}"
+                lines.append(f"| Address | {street} |")
+                lines.append(f"| | {city_state_zip} |")
+
     # Quantitative Data
     lines.extend([
         "",