Commercial Team

Commercial Banking
MCP Services

Ten MCP services for the Commercial Banking team — each callable by any AI assistant to deliver business prospect identification, industry vertical analysis, SME health monitoring, trade finance discovery, and B2B ecosystem mapping using web scraping, AI analysis, and the 100M+ domain database.

1Business Prospect Identifier

Queries the 100M domain database to find businesses matching an ideal customer profile by IAB category, PageRank range, country, and domain age. Returns an enriched prospect list with web-derived business intelligence for targeted commercial outreach.

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MCP Tool Definition
Domain DB Web Scraping GPT-4o
business_prospect_identifier iab_category: string — IAB category to target (e.g. "IAB3 - Business") pagerank_min: number — Minimum PageRank threshold (0-10) pagerank_max: number — Maximum PageRank threshold (0-10) country: string — ISO country code filter (e.g. "US", "GB") domain_age_min_days: integer — Minimum domain age in days (established businesses) limit: integer — Max results to return (default: 50)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Query 100M domain DB with ICP filters: IAB category, PageRank range, country, domain age Step 2: Retrieve matching domains with metadata (PageRank, age, category, country) Step 3: Scrape homepage + /about for each prospect to extract business description Step 4: AI classifies business size signals: employee count indicators, office locations, product breadth Step 5: Score prospect fit against ICP criteria (0-100 match score) Step 6: Enrich with contact signals: /contact page, email patterns, phone numbers Step 7: Return ranked prospect list with enrichment data and match scores
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Example Output
MCP RESPONSE — business_prospect_identifier ════════════════════════════════════════════════════════════ ICP FILTER: IAB: Manufacturing | PageRank: 3.0–6.0 | Country: US | Age: 1,000+ days MATCHES: 4,218 domains found | Top 50 enriched TOP PROSPECTS: precisionmfg-solutions.com | Match: 94/100 PageRank: 4.8 | Age: 3,247 days | IAB: Manufacturing Business: CNC precision machining, aerospace & defense contracts Size signals: 85+ employees (career page), 3 facility locations Revenue indicators: ISO 9001, AS9100 certified — enterprise client base Contact: [email protected] | (312) 555-0847 midwestpackaging.com | Match: 91/100 PageRank: 4.2 | Age: 4,891 days | IAB: Manufacturing Business: Custom corrugated packaging, food-grade solutions Size signals: 120+ employees, 2 plants (IL, OH) Revenue indicators: SQF certified, major retail partnerships listed Contact: [email protected] | (614) 555-1293 advancedcoatings-usa.com | Match: 82/100 PageRank: 3.6 | Age: 2,104 days | IAB: Manufacturing Business: Industrial powder coating, surface treatment services Size signals: 40-60 employees estimated, single facility Revenue indicators: Regional client base, no enterprise certifications Contact: [email protected] | (937) 555-0461 PROSPECT SUMMARY: Top tier (90+): 12 prospects | Avg PageRank: 4.5 | Avg age: 3,800 days Mid tier (75-89): 23 prospects | Avg PageRank: 3.8 | Avg age: 2,400 days Lower tier (<75): 15 prospects | Avg PageRank: 3.2 | Avg age: 1,600 days

2Industry Vertical Analyzer

Analyzes an entire IAB category from the domain database, scraping sample sites to assess industry health, growth trends, competitive density, and lending opportunity — giving commercial bankers a data-driven view of sector attractiveness.

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MCP Tool Definition
Domain DB Web Scraping GPT-4o
industry_vertical_analyzer iab_category: string — IAB category to analyze (e.g. "IAB13 - Personal Finance") country: string — ISO country code to scope analysis (default: "US") sample_size: integer — Number of sites to scrape for deep analysis (default: 100) pagerank_tier: string — "all", "top", "mid", "emerging" — segment to focus on
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Query 100M domain DB for all domains in target IAB category + country Step 2: Calculate sector statistics: total domains, PageRank distribution, age distribution Step 3: Sample N domains across PageRank tiers and scrape /careers, /products, /pricing Step 4: AI analyzes hiring activity, product breadth, and pricing patterns across sample Step 5: Assess competitive density (domains per PageRank band), growth signals, consolidation patterns Step 6: Calculate lending opportunity score based on sector health indicators Step 7: Generate vertical intelligence report with market sizing and opportunity ranking
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Example Output
MCP RESPONSE — industry_vertical_analyzer ════════════════════════════════════════════════════════════ VERTICAL: IAB13 — Personal Finance | Country: US | Sample: 100 sites SECTOR OVERVIEW: Total domains in DB: 28,471 | Avg PageRank: 3.4 | Median age: 2,847 days Top tier (PR 6+): 312 | Mid tier (PR 3-6): 8,947 | Emerging (PR <3): 19,212 GROWTH INDICATORS: Hiring activity: +34% active job postings vs. 6 months ago New entrants: 1,847 domains < 365 days old (6.5% of sector) Product expansion: 68% of sampled sites added new features in 90 days Pricing trends: 42% raised prices, 18% added free tiers (competitive pressure) COMPETITIVE DENSITY: Concentration ratio (top 10): 41% of total sector PageRank Fragmentation index: HIGH — opportunity for consolidation lending Barrier to entry: MODERATE — regulatory licensing required LENDING OPPORTUNITY ASSESSMENT: Overall score: 78/100 — ATTRACTIVE Growth lending: Strong expansion signals, high demand for working capital M&A financing: Fragmented market ripe for consolidation plays Risk factor: Regulatory uncertainty in digital lending subsector Equipment finance: 72% of sites show tech infrastructure investment RECOMMENDATION: Increase sector allocation — strong fundamentals with growth tailwinds

3SME Health Dashboard

Scrapes SME borrower websites to build health dashboards using digital proxy signals: career page activity, product offerings, digital sophistication, and content freshness — providing continuous monitoring of small business health without financial statements.

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MCP Tool Definition
Web Scraping GPT-4o Domain DB Vision AI
sme_health_dashboard domain: string — SME borrower domain to monitor metrics: array — ["careers","products","freshness","sophistication","social"] signals to track compare_baseline: boolean — Compare against previous snapshot (default: true) peer_benchmark: boolean — Include sector peer comparison (default: true)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Scrape SME website: homepage, /careers, /products, /blog, /about, /contact Step 2: Screenshot homepage for visual sophistication scoring via Vision AI Step 3: Analyze career page: active postings, departments, growth signals Step 4: Assess product catalog depth, pricing page status, service breadth Step 5: Measure content freshness: last blog post, news update, copyright year Step 6: Score digital sophistication: SSL, mobile responsive, load speed, modern stack Step 7: Compare against baseline and sector peers from domain DB
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Example Output
MCP RESPONSE — sme_health_dashboard ════════════════════════════════════════════════════════════ greenlawncare-pros.com | IAB: Home & Garden | PageRank: 2.1 | Age: 1,847 days HEALTH SCORE: 72/100 (Healthy) | Baseline: 68/100 (+4) CAREER SIGNALS: Score: 78/100 Active postings: 6 roles (was 3 at baseline) — growing Roles: 2 crew leads, 2 technicians, 1 office manager, 1 sales Signal: Expanding workforce, adding management layer PRODUCT/SERVICE SIGNALS: Score: 81/100 Services listed: 12 services (was 8) — 50% expansion New additions: Tree trimming, irrigation installation, holiday lighting Pricing: Service packages with online quoting — revenue sophistication CONTENT FRESHNESS: Score: 58/100 Last blog post: 47 days ago (was weekly, now irregular) Photo gallery: Updated with 2026 project photos Copyright footer: 2026 (current) DIGITAL SOPHISTICATION: Score: 71/100 SSL: Active | Mobile: Responsive | Load: 3.2s (slow) Online booking: Present — ServiceTitan integration detected Reviews integration: Google Reviews widget (4.7 stars, 284 reviews) PEER COMPARISON (Home & Garden SMEs, n=847): Percentile: 67th | Sector median: 61/100 | This SME: 72/100 ASSESSMENT: Healthy growth trajectory — service expansion and hiring indicate revenue growth

4Trade Finance Entity Finder

Identifies import/export businesses from the domain database by scraping for trade-related content — shipping terms, customs documentation, international trade references — and uses AI classification to build qualified trade finance prospect lists.

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MCP Tool Definition
Domain DB Web Scraping GPT-4o
trade_finance_entity_finder iab_categories: array — IAB categories to search (e.g. ["Manufacturing","Business"]) trade_type: string — "import", "export", or "both" (default: "both") country: string — ISO country code for business domicile trade_corridors: array — Target trade corridors (e.g. ["US-CN","US-DE","US-MX"]) min_pagerank: number — Minimum PageRank for established businesses
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Query domain DB for businesses in target IAB categories and country Step 2: Scrape homepage, /about, /services, /products for trade-related keywords Step 3: AI classifies trade activity: shipping terms (FOB, CIF, EXW), customs references, port mentions Step 4: Detect trade corridors from country mentions, language variants, currency references Step 5: Score trade finance relevance (0-100) based on content signals Step 6: Estimate trade volume tier from product catalog depth and geographic reach
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Example Output
MCP RESPONSE — trade_finance_entity_finder ════════════════════════════════════════════════════════════ SEARCH: IAB: Manufacturing, Business | Country: US | Trade: both | Corridors: US-CN, US-DE SCANNED: 12,847 domains | Trade entities identified: 1,247 HIGH-CONFIDENCE TRADE ENTITIES: pacificrim-imports.com | Relevance: 97/100 Type: IMPORTER | Corridor: US-CN, US-TW Trade signals: FOB Shenzhen terms, customs broker references, HS codes listed Products: Consumer electronics components, 2,400+ SKUs Volume tier: LARGE — multi-container shipments indicated PageRank: 4.1 | Age: 5,218 days germanauto-parts.com | Relevance: 94/100 Type: IMPORTER | Corridor: US-DE Trade signals: Incoterms CIF mentioned, EU CE marking references, Hamburg port Products: OEM automotive parts, precision bearings, 800+ SKUs Volume tier: MEDIUM — regular monthly shipments indicated PageRank: 3.8 | Age: 3,691 days texasagrexports.com | Relevance: 91/100 Type: EXPORTER | Corridor: US-CN, US-MX Trade signals: Export licenses mentioned, USDA certification, fumigation certs Products: Agricultural commodities, grain, cotton Volume tier: LARGE — bulk cargo, rail-to-port logistics referenced PageRank: 3.4 | Age: 4,102 days TRADE CORRIDOR SUMMARY: US-CN: 487 entities (39%) | US-DE: 218 entities (17%) US-MX: 312 entities (25%) | Other: 230 entities (19%) LENDING OPPORTUNITY: $340M estimated LC/trade finance demand from top 100 entities

5Cash Flow Signal Detector

Monitors business pricing pages for changes — new tiers added, price increases, discontinued products, plan restructuring — as early revenue and cash flow signals. Uses web scraping with AI comparison to detect meaningful pricing shifts.

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MCP Tool Definition
Web Scraping GPT-4o Vision AI
cash_flow_signal_detector domain: string — Target business domain to monitor pages: array — Pages to watch: ["/pricing","/products","/plans","/services"] baseline_snapshot: string — ISO date of previous snapshot to compare against sensitivity: string — "low", "medium", "high" — change detection sensitivity
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Scrape current /pricing, /products, /plans pages and capture text + screenshots Step 2: Retrieve baseline snapshot from previous scan for comparison Step 3: AI text comparison: detect price point changes, tier additions/removals, term changes Step 4: Vision AI screenshot diff: visual layout changes, removed elements, new banners Step 5: Classify signal type: PRICE_INCREASE | PRICE_CUT | NEW_TIER | DISCONTINUED | RESTRUCTURE Step 6: Assess cash flow implications: positive (price power), negative (desperation), neutral
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Example Output
MCP RESPONSE — cash_flow_signal_detector ════════════════════════════════════════════════════════════ cloudpayroll-saas.com | Baseline: 2025-12-01 | Current: 2026-03-02 CHANGES DETECTED: 4 signals SIGNAL 1: PRICE_INCREASE Page: /pricing Change: Professional tier $149/mo → $179/mo (+20.1% increase) Implication: Pricing power — confident in value delivery SIGNAL 2: NEW_TIER Page: /pricing Change: New "Enterprise Plus" tier added at $499/mo Features: Custom integrations, dedicated CSM, SLA guarantees Implication: Upmarket expansion — pursuing larger ACV deals SIGNAL 3: DISCONTINUED Page: /products Change: "PayrollLite" free tier removed entirely Previous: Free plan for up to 5 employees, used as lead gen Implication: Possible cash pressure — cutting free tier to force conversions SIGNAL 4: RESTRUCTURE Page: /pricing Change: Annual billing discount increased from 15% → 30% New banner: "Lock in annual pricing before April 1" Implication: Aggressive push for upfront cash — possible cash flow need COMPOSITE CASH FLOW ASSESSMENT: Revenue direction: MIXED — price increases offset by desperation signals Confidence level: MEDIUM Key concern: 30% annual discount + free tier removal suggests cash pressure Positive: Enterprise tier launch shows product maturity RECOMMENDATION: Request updated financials — mixed signals warrant closer review

6Business Expansion Tracker

Detects geographic or product expansion by scraping /careers for new office locations, /products for new lines, and /press for expansion announcements — using AI analysis to identify businesses that may need growth financing.

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MCP Tool Definition
Web Scraping GPT-4o Domain DB
business_expansion_tracker domains: array — List of business domains to monitor for expansion expansion_types: array — ["geographic","product","workforce","market"] signals to track lookback_days: integer — Compare against N days ago baseline (default: 90) min_confidence: number — Minimum AI confidence threshold 0-1 (default: 0.7)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Scrape /careers for new location mentions, regional job postings, office addresses Step 2: Scrape /products and /services for new offerings, categories, or verticals Step 3: Scrape /press and /news for expansion announcements, funding rounds, partnership reveals Step 4: AI extracts geographic entities and maps new vs. existing locations Step 5: Compare product/service pages against baseline to detect catalog growth Step 6: Score expansion velocity and estimate capital needs for each expansion type
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Example Output
MCP RESPONSE — business_expansion_tracker ════════════════════════════════════════════════════════════ MONITORED: 340 commercial borrower domains | Lookback: 90 days EXPANSION DETECTED: 47 businesses showing expansion signals TOP EXPANSION SIGNALS: solarinstall-national.com | Confidence: 0.96 GEOGRAPHIC: Career postings in 4 new states (TX, FL, AZ, NV) — was 6 states, now 10 WORKFORCE: 38 open roles (was 12) — 217% increase PRESS: "Series B: $42M to Fuel National Expansion" posted 2026-01-22 Estimated capital need: $15-25M equipment + working capital line freshmeals-delivery.com | Confidence: 0.91 PRODUCT: 3 new meal plan categories added (Keto, Family, Corporate Catering) GEOGRAPHIC: Delivery zone map expanded from 2 cities to 7 cities WORKFORCE: 14 driver positions, 4 kitchen staff — new market buildout Estimated capital need: $3-5M working capital + cold chain equipment techstaffing-partners.com | Confidence: 0.84 MARKET: New "/healthcare-staffing" page launched (vertical expansion) GEOGRAPHIC: New office address in Nashville, TN on /contact page WORKFORCE: 6 new recruiter roles posted (market-specific) Estimated capital need: $2-4M working capital for payroll float EXPANSION VELOCITY RANKING: Rapid (5+ signals): 8 businesses — immediate outreach recommended Moderate (3-4 signals): 17 businesses — monitor and engage Early (1-2 signals): 22 businesses — add to pipeline ESTIMATED TOTAL LENDING OPPORTUNITY: $180-260M across 47 expanding businesses

7Franchise Network Mapper

Discovers franchise networks by finding domains with similar branding patterns, linked franchise pages, and location pages across the 100M domain database. Maps network size, geographic spread, and franchise health indicators for franchise lending opportunities.

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MCP Tool Definition
Domain DB Web Scraping GPT-4o Vision AI
franchise_network_mapper brand_domain: string — Franchisor's main domain (e.g. "quickoilchange.com") brand_name: string — Brand name for pattern matching across domains include_health: boolean — Scrape franchise locations for individual health scoring country: string — Limit search to specific country (default: all)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Search domain DB for domains containing brand name patterns and variants Step 2: Scrape franchisor's /locations or /find-a-location page for official network map Step 3: Cross-reference discovered domains with official location list Step 4: Vision AI compares screenshots for branding consistency across franchise sites Step 5: Scrape individual franchise sites for health indicators: active content, reviews, services Step 6: Map geographic coverage, density clusters, and white-space opportunities Step 7: Calculate network health score and franchise lending risk profile
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Example Output
MCP RESPONSE — franchise_network_mapper ════════════════════════════════════════════════════════════ quickoilchange.com | Brand: "Quick Oil Change" NETWORK MAP: Franchisor domain: PageRank: 5.4 | Age: 7,281 days Official locations listed: 287 Franchise domains discovered in DB: 214 (74.6% have own domains) Branding consistency: 91% — strong brand enforcement GEOGRAPHIC DISTRIBUTION: Southeast US: 89 locations (31%) — highest density Midwest US: 67 locations (23%) — established markets Texas: 48 locations (17%) — rapid growth corridor Northeast US: 34 locations (12%) — underpenetrated West Coast: 27 locations (9%) — significant white space Other: 22 locations (8%) FRANCHISE HEALTH INDICATORS: Active websites (updated in 90 days): 198 of 214 (92.5%) Google Review avg: 4.3 stars (n=214 locations sampled) Locations showing distress signals: 8 (3.7%) — 3 with expired SSL | 2 with "temporarily closed" | 3 with stale content NETWORK GROWTH: New locations (last 12 months): +31 (10.8% growth) Closed locations detected: 6 (2.1% attrition) Net growth: +25 locations FRANCHISE LENDING PROFILE: Network maturity: ESTABLISHED — 20-year brand, stable growth Per-unit lending opportunity: $350K-$500K (equipment + buildout) White space opportunity: $25-35M for 50-70 new units in underserved regions Risk level: LOW — strong brand, low attrition, high consistency

8B2B Partnership Intelligence

Extracts partnership data from /partners, /integrations, and /customers pages across multiple companies to map B2B ecosystems and identify cross-selling opportunities — revealing network effects and relationship patterns for commercial banking.

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MCP Tool Definition
Web Scraping GPT-4o Domain DB
b2b_partnership_intelligence seed_domains: array — Starting domains to map partnerships from depth: integer — Network depth: 1 = direct partners, 2 = partners of partners partnership_types: array — ["technology","reseller","channel","integration","customer"] min_pagerank: number — Filter partners by minimum PageRank
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Scrape /partners, /integrations, /customers, /case-studies for each seed domain Step 2: AI extracts company names, logos, and partnership descriptions from pages Step 3: Resolve company names to domains using domain DB fuzzy matching Step 4: Classify partnership type: technology, reseller, channel, integration, customer Step 5: For depth 2+: scrape discovered partners' partnership pages recursively Step 6: Build network graph: nodes (companies) and edges (partnership types) Step 7: Identify cross-sell clusters, network hubs, and orphan prospects
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Example Output
MCP RESPONSE — b2b_partnership_intelligence ════════════════════════════════════════════════════════════ SEED DOMAINS: 5 existing commercial clients | Depth: 2 | Partners found: 312 NETWORK MAP: Total companies mapped: 312 Partnership edges: 847 Already clients: 5 (seed) | Prospects: 307 new companies TOP CROSS-SELL CLUSTERS: Cluster A: E-commerce Ecosystem (68 companies) Hub: shopifypartners-agency.com (PageRank: 5.2, connected to 34 others) Partners: Payment processors, shipping APIs, inventory management SaaS Overlap with clients: 3 existing clients are in this cluster Cross-sell opportunity: 65 warm intros via existing relationships Cluster B: Healthcare Tech Stack (41 companies) Hub: healthdata-platform.com (PageRank: 4.8, connected to 22 others) Partners: EHR integrations, telehealth, medical billing, compliance tools Overlap with clients: 1 existing client only Cross-sell opportunity: 40 prospects, strong vertical alignment Cluster C: Supply Chain / Logistics (53 companies) Hub: freightmanage-pro.com (PageRank: 4.5, connected to 28 others) Partners: Warehouse mgmt, fleet tracking, customs brokers, 3PL providers Overlap with clients: 2 existing clients are in this cluster Cross-sell opportunity: 51 prospects with referral paths HIGHEST-VALUE PROSPECTS (not yet clients): healthdata-platform.com — Hub node, 22 connections, PR: 4.8 | Priority: HIGH freightmanage-pro.com — Hub node, 28 connections, PR: 4.5 | Priority: HIGH inventorycloud-saas.com — Bridge node, 3 clusters, PR: 4.1 | Priority: MEDIUM ESTIMATED REVENUE: $2.4M annual fee income from top 20 partnership-mapped prospects

9Commercial Insurance Needs Assessor

Scrapes business websites to assess insurance needs based on AI analysis of industry type, employee count signals, physical location indicators, product liability exposures, and operational complexity — enabling insurance product cross-selling to commercial clients.

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MCP Tool Definition
Web Scraping GPT-4o Domain DB Vision AI
commercial_insurance_needs_assessor domain: string — Business domain to assess insurance needs coverage_types: array — ["general_liability","property","workers_comp","cyber","product_liability","professional"] include_estimate: boolean — Include premium range estimate (default: true) industry_benchmark: boolean — Compare against industry averages (default: true)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Query domain DB for IAB category, country, PageRank (business size proxy) Step 2: Scrape homepage, /about, /services, /locations, /careers, /products Step 3: AI analyzes: industry hazard class, physical operations, product types, service scope Step 4: Estimate employee count from career page signals and office locations Step 5: Assess product liability exposure from product descriptions and claims/warranty language Step 6: Score coverage needs by type and estimate premium ranges based on industry benchmarks
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Example Output
MCP RESPONSE — commercial_insurance_needs_assessor ════════════════════════════════════════════════════════════ mountainridge-construction.com | IAB: Real Estate | PageRank: 3.7 | Age: 4,218 days BUSINESS PROFILE (AI-inferred): Industry: Commercial construction — general contractor Estimated employees: 75-120 (22 career postings, 3 office locations) Physical locations: Denver (HQ), Colorado Springs, Fort Collins Operations: Heavy equipment, high-rise, residential, renovation Certifications found: OSHA 30-hour, EPA Lead-Safe, bonded & insured mention COVERAGE NEEDS ASSESSMENT: GENERAL LIABILITY: CRITICAL — HIGH NEED Risk factors: Construction operations, subcontractor oversight, public exposure Current signal: "Fully bonded and insured" stated on /about page Estimated premium: $45,000-$65,000/year WORKERS COMPENSATION: CRITICAL — HIGH NEED Risk factors: Construction class codes, 75-120 employees, physical labor Career page: Roles include roofers, framers, heavy equipment operators Estimated premium: $120,000-$180,000/year COMMERCIAL PROPERTY: MODERATE NEED 3 office/warehouse locations detected, equipment yard mentioned Estimated premium: $18,000-$28,000/year PROFESSIONAL LIABILITY (E&O): MODERATE NEED Design-build services mentioned, architectural oversight Estimated premium: $8,000-$15,000/year CYBER LIABILITY: LOW NEED Limited digital operations, basic website, no customer portal Estimated premium: $2,000-$4,000/year TOTAL ESTIMATED INSURANCE SPEND: $193,000-$292,000/year CROSS-SELL OPPORTUNITY: HIGH — construction firms have significant multi-line insurance needs

10Working Capital Cycle Analyzer

Analyzes business websites for working capital indicators: inventory levels inferred from product catalog depth, receivables signals from payment terms displayed on-site, and payables patterns — helping size working capital facilities with web-derived intelligence.

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MCP Tool Definition
Web Scraping GPT-4o Domain DB
working_capital_cycle_analyzer domain: string — Business domain to analyze working capital signals analysis_depth: string — "quick" (homepage/pricing only) or "deep" (full site crawl) industry_context: boolean — Include industry-typical working capital benchmarks (default: true) include_seasonality: boolean — Detect seasonal patterns from content/product changes
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Scrape /products, /shop, /catalog to assess inventory breadth and depth Step 2: Analyze /terms, /payment, /faq for payment terms, net days, credit policies Step 3: Scrape /suppliers, /partners, /about for supply chain and payables indicators Step 4: AI classifies business model: inventory-heavy, service, project-based, or hybrid Step 5: Estimate working capital cycle: inventory days + receivable days - payable days Step 6: Compare against industry benchmarks from domain DB sector analysis Step 7: Size appropriate working capital facility and recommend structure
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Example Output
MCP RESPONSE — working_capital_cycle_analyzer ════════════════════════════════════════════════════════════ industrialparts-depot.com | IAB: Business | PageRank: 4.0 | Age: 5,104 days BUSINESS MODEL: Inventory-Heavy Distribution INVENTORY SIGNALS: Product catalog: 8,400+ SKUs across 24 categories "In Stock" indicators: Present on 89% of product pages Warehouse mentions: 2 distribution centers (Dallas, TX and Atlanta, GA) Estimated inventory days: 55-70 days (industry avg: 45 days) Signal: Deep catalog suggests high inventory carrying costs RECEIVABLES SIGNALS: Payment terms page: "Net 30 for approved accounts, Net 45 for government" Credit application: Online credit application form detected B2B indicators: Purchase orders accepted, bulk pricing tiers listed Estimated receivable days: 35-45 days Signal: Extended terms to government customers increase DSO PAYABLES SIGNALS: Supplier count: 47 brand logos on /brands page Import indicators: "Ships from manufacturer" on some products — drop-ship mix Estimated payable days: 30-40 days (standard distributor terms) WORKING CAPITAL CYCLE ESTIMATE: Inventory days: 55-70 + Receivable days: 35-45 - Payable days: 30-40 Cash conversion cycle: 60-75 days Industry benchmark: 40-55 days | This business: Above average SEASONALITY DETECTED: Product pages show "Spring Sale" and "Winter Inventory Clearance" patterns Career postings spike in Q1 (seasonal warehouse staff) Peak WC need: Q4 (pre-stock for spring) — estimated 90-day cycle FACILITY RECOMMENDATION: Type: Asset-based revolving line of credit Recommended size: $4-6M (based on estimated inventory + receivables) Structure: Borrowing base: 80% eligible receivables + 50% eligible inventory Seasonal component: $1.5M seasonal overline for Q4 pre-stocking
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