Strategy Team

Market Research & Strategy
MCP Services

Ten MCP services for the Market Research & Strategy team — each callable by any AI assistant to deliver competitive intelligence dashboards, market sizing estimates, pricing intelligence, customer segment analysis, and industry trend reports using web scraping, AI analysis, and the 100M+ domain database.

1Competitive Intelligence Dashboard

Scrapes competitor bank websites for comprehensive comparison: product offerings, pricing, branch networks, and digital capabilities. Builds a structured comparison matrix for strategic positioning analysis.

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MCP Tool Definition
Web Scraping GPT-4o Domain DB Vision AI
competitive_intelligence_dashboard competitors: array — List of competitor bank domains (e.g. ["chase.com","bankofamerica.com"]) dimensions: array — ["products","pricing","branches","digital","rewards"] to compare screenshot: boolean — Include visual comparison of competitor homepages (default: true)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Scrape each competitor's /products, /pricing, /rates, /locations, /digital pages Step 2: Screenshot competitor homepages and key product pages for visual analysis Step 3: Query domain DB for each competitor's PageRank, domain age, country Step 4: Send all scraped content to GPT-4o with competitive analysis prompt Step 5: AI extracts: product lineup, rate structures, branch counts, digital features Step 6: Build structured comparison matrix across all dimensions Step 7: Identify competitive gaps, unique differentiators, and strategic opportunities
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Example Output
MCP RESPONSE — competitive_intelligence_dashboard ════════════════════════════════════════════════════════════ COMPETITORS ANALYZED: 6 banks | Dimensions: products, pricing, digital COMPARISON MATRIX — PRODUCT OFFERINGS: Checking Savings Mortgage Auto Credit Invest chase.com 5 tiers 3 8 types Yes 12 Full bankofamerica.com 4 tiers 3 7 types Yes 10 Full wellsfargo.com 4 tiers 2 6 types Yes 9 Full ally.com 2 tiers 3 3 types Yes 2 Robo capitalone.com 3 tiers 2 4 types Yes 14 Partial YOUR BANK 2 tiers 1 3 types No 4 None PRICING COMPARISON — SAVINGS APY: ally.com: 4.20% APY (online-only, no minimum) capitalone.com: 3.80% APY ($0 minimum, 360 Performance) chase.com: 0.01% APY (standard) / 4.00% APY (CD) YOUR BANK: 0.05% APY — below 5 of 6 competitors DIGITAL CAPABILITIES: Mobile check deposit: All competitors offer this | Your bank: Yes Zelle integration: 5/6 have it | Your bank: No AI chatbot: 3/6 have it | Your bank: No Real-time alerts: All 6 offer | Your bank: Yes STRATEGIC GAPS IDENTIFIED: 1. No investment platform — all major competitors offer some form 2. No auto lending — missing $1.4T market segment 3. Limited credit card lineup (4 vs. avg 9.5 competitor cards) 4. No Zelle or peer-to-peer payments integration

2Market Sizing Estimator

Uses the domain database to estimate market size by counting and analyzing domains in a specific IAB category, country, and PageRank range. AI extrapolates market potential from domain density, authority distribution, and competitive concentration.

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MCP Tool Definition
Domain DB GPT-4o Web Scraping
market_sizing_estimator iab_category: string — IAB category to analyze (e.g. "IAB13 — Personal Finance") country: string — ISO country code to filter (e.g. "US","GB","DE") pagerank_min: number — Minimum PageRank threshold (default: 2.0) pagerank_max: number — Maximum PageRank threshold (default: 10.0)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Query 100M domain DB for all domains matching IAB category + country Step 2: Filter by PageRank range to focus on relevant market players Step 3: Segment by PageRank tiers: enterprise (7+), mid-market (4-7), SMB (2-4) Step 4: Sample top 50 domains and scrape homepages for revenue/employee signals Step 5: AI extrapolates total addressable market from domain density patterns Step 6: Calculate market concentration (HHI), growth indicators, and entry barriers Step 7: Generate market sizing report with TAM/SAM/SOM estimates
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Example Output
MCP RESPONSE — market_sizing_estimator ════════════════════════════════════════════════════════════ QUERY: IAB13 — Personal Finance | Country: US | PageRank: 2.0–10.0 DOMAIN DATABASE RESULTS: Total domains in category: 14,872 PageRank 7+ (Enterprise): 127 domains PageRank 4-7 (Mid-Market): 2,341 domains PageRank 2-4 (SMB/Startup): 12,404 domains MARKET SIZING ESTIMATES: Total Addressable Market (TAM): $847B Based on: 127 enterprise domains x avg $3.2B revenue signal Plus: 2,341 mid-market x avg $85M revenue signal Serviceable Available Market (SAM): $124B Filtered to: digital banking, lending, wealth management Geographic focus: Top 25 US metro areas Serviceable Obtainable Market (SOM): $8.2B Realistic capture: 6.6% of SAM over 5 years MARKET CONCENTRATION: HHI Index: 1,847 (Moderately concentrated) Top 5 domains control: 34% of total PageRank authority chase.com (PR: 8.4) | bankofamerica.com (PR: 8.2) | wellsfargo.com (PR: 8.0) GROWTH INDICATORS: New domains (last 12 months): +1,247 (8.4% growth rate) Avg domain age in category: 4,218 days (mature market) Fintech disruption signal: 38% of new entries are under 2 years old

3Customer Segment Identifier

Analyzes bank and fintech websites using AI to identify target customer segments from content, imagery, pricing tiers, and product positioning. Reveals who competitors are targeting and underserved segments in the market.

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MCP Tool Definition
Web Scraping GPT-4o Vision AI Domain DB
customer_segment_identifier domains: array — List of bank/fintech domains to analyze segment_depth: string — "basic" (demographics) or "deep" (psychographics + behaviors) include_visual: boolean — Analyze imagery for demographic targeting signals (default: true)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Scrape homepage, /personal, /business, /wealth, /about for each domain Step 2: Screenshot hero sections and product pages for visual demographic analysis Step 3: Extract pricing tiers, product names, marketing language via GPT-4o Step 4: Vision AI analyzes imagery: age groups, lifestyle cues, diversity signals Step 5: Classify segments: mass-market, affluent, HNWI, SMB, enterprise, Gen-Z, etc. Step 6: Map competitive segment coverage and identify whitespace opportunities
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Example Output
MCP RESPONSE — customer_segment_identifier ════════════════════════════════════════════════════════════ DOMAINS ANALYZED: 8 banks/fintechs | Depth: deep SEGMENT MAP BY COMPETITOR: chase.com — BROAD COVERAGE Primary: Mass-market consumers (checking, savings messaging) Secondary: Affluent ($250K+ journey, Sapphire branding) Tertiary: Small business (dedicated /business section) Visual cues: Multi-generational imagery, urban professionals chime.com — NARROW FOCUS Primary: Underbanked / fee-averse millennials Secondary: Gig economy workers (early paycheck messaging) Visual cues: Young adults, casual imagery, mobile-first design Language: "No hidden fees", "Get paid early", "Fee-free" svb.com — NICHE SPECIALIST Primary: Tech startups and VC-backed companies Secondary: Innovation economy executives Visual cues: Tech office imagery, startup culture references Language: "Innovation economy", "venture lending", "startup banking" SEGMENT COVERAGE HEAT MAP: Mass Affluent HNWI GenZ SMB Enterprise Underbanked chase.com ███ ███ ██ █ ███ ██ ░ chime.com █ ░ ░ ███ ░ ░ ███ svb.com ░ ░ ░ ░ ███ ███ ░ UNDERSERVED SEGMENTS IDENTIFIED: 1. Rural small business owners — minimal digital banking options 2. Semi-retired gig workers (55+) — gap between fintech and traditional 3. Micro-business (1-5 employees) — too small for commercial, too complex for personal

4Brand Perception Analyzer

Scrapes company web content, blog, press releases, and case studies to analyze brand positioning: premium vs. value, innovation vs. stability, corporate vs. retail focus. Quantifies brand perception signals from digital content.

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MCP Tool Definition
Web Scraping GPT-4o Vision AI
brand_perception_analyzer domain: string — Target company domain to analyze axes: array — ["premium_value","innovation_stability","corporate_retail","digital_traditional"] compare_to: array — Optional competitor domains for relative positioning
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Scrape homepage, /about, /blog, /press, /case-studies, /careers Step 2: Screenshot key pages for visual brand assessment (colors, imagery, design) Step 3: GPT-4o analyzes language tone: aspirational, accessible, authoritative, friendly Step 4: Vision AI evaluates visual brand signals: color palette, photography style, UX Step 5: Score brand on each axis from -1.0 (left) to +1.0 (right) Step 6: Compare against competitor positioning if provided Step 7: Generate brand perception map with quadrant positioning
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Example Output
MCP RESPONSE — brand_perception_analyzer ════════════════════════════════════════════════════════════ firstnationalbank.com | Axes: 4 | Competitors: 3 BRAND POSITIONING SCORES: Premium ←——————————→ Value firstnationalbank.com: +0.12 (Slightly premium) chase.com: +0.45 (Premium-leaning) ally.com: -0.72 (Strong value positioning) Innovation ←——————————→ Stability firstnationalbank.com: +0.61 (Stability-focused) chase.com: +0.08 (Balanced) chime.com: -0.83 (Innovation-forward) Corporate ←——————————→ Retail firstnationalbank.com: +0.34 (Retail-leaning) jpmorgan.com: -0.91 (Strong corporate) chase.com: +0.55 (Consumer-focused) Digital ←——————————→ Traditional firstnationalbank.com: +0.67 (Traditional-heavy) ally.com: -0.92 (Fully digital) chase.com: -0.15 (Slightly digital-leaning) CONTENT SIGNALS: Blog tone: Formal, educational — "financial literacy" focus Press releases: Community-oriented — "local impact", "hometown banking" Case studies: None found — missing trust-building content Visual identity: Stock photography, conservative color palette (navy/gold) BRAND GAP ANALYSIS: Positioned as traditional community bank in an increasingly digital market No innovation narrative — competitors averaging -0.3 on innovation axis Strong community perception could be leveraged for "digital + local" strategy

5Geographic Expansion Opportunity Finder

Uses domain database country data to identify geographic markets with low competitor density in specific IAB categories. Assesses market entry opportunity by analyzing domain authority distribution, language coverage, and competitive gaps across regions.

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MCP Tool Definition
Domain DB GPT-4o Web Scraping
geographic_expansion_finder iab_category: string — IAB category to analyze for expansion (e.g. "IAB13 — Personal Finance") exclude_countries: array — Countries already operating in (e.g. ["US","CA"]) min_population: integer — Minimum country population threshold (default: 5000000) max_results: integer — Top N opportunities to return (default: 15)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Query 100M domain DB for all domains in IAB category, grouped by country Step 2: Calculate competitor density: domains per million population per country Step 3: Analyze PageRank distribution per country (mature vs. nascent markets) Step 4: Scrape top 5 competitors in each low-density market for product analysis Step 5: AI assesses market entry barriers: language, regulation, digital readiness Step 6: Score each geography: opportunity = (market_size / competitor_density) x readiness Step 7: Rank and return top opportunities with entry strategy recommendations
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Example Output
MCP RESPONSE — geographic_expansion_finder ════════════════════════════════════════════════════════════ CATEGORY: IAB13 — Personal Finance | Excluded: US, CA, GB TOP GEOGRAPHIC OPPORTUNITIES: 1. BRAZIL (BR) — Opportunity Score: 92/100 Domains in category: 847 | Pop: 215M | Density: 3.9 per 1M (LOW) Avg PageRank: 3.1 (nascent market — low authority incumbents) Top competitor: nubank.com.br (PR: 6.8) — dominant but alone Entry barriers: Portuguese language, Central Bank licensing Opportunity: Massive underbanked population, rapid digital adoption 2. INDONESIA (ID) — Opportunity Score: 87/100 Domains in category: 312 | Pop: 275M | Density: 1.1 per 1M (VERY LOW) Avg PageRank: 2.4 (very early stage market) Top competitor: bca.co.id (PR: 5.9) Entry barriers: Indonesian language, OJK regulatory approval Opportunity: Largest unbanked population in SE Asia, mobile-first market 3. POLAND (PL) — Opportunity Score: 74/100 Domains in category: 624 | Pop: 38M | Density: 16.4 per 1M (MODERATE) Avg PageRank: 3.8 (developing market) Top competitor: mbank.pl (PR: 5.7) Entry barriers: EU passporting available, English widely spoken Opportunity: EU market with growing digital banking adoption MARKET ENTRY DENSITY COMPARISON: US (baseline): 42.1 domains per 1M pop | Saturated UK: 38.7 domains per 1M pop | Saturated Brazil: 3.9 domains per 1M pop | High opportunity Indonesia: 1.1 domains per 1M pop | Highest opportunity Poland: 16.4 domains per 1M pop | Moderate opportunity

6Product-Market Fit Analyzer

Scrapes product pages and /pricing to analyze product-market fit signals: pricing tier adoption, feature depth, customer testimonials, and integration ecosystem size. Reveals how well competitors' products resonate with their target markets.

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MCP Tool Definition
Web Scraping GPT-4o Vision AI Domain DB
product_market_fit_analyzer domain: string — Target company domain to analyze signals: array — ["pricing_tiers","integrations","testimonials","feature_depth","social_proof"] benchmark_category: string — IAB category for peer comparison
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Scrape /products, /pricing, /features, /integrations, /customers pages Step 2: Extract pricing tiers, feature lists, integration counts, testimonials Step 3: Screenshot product pages for visual polish and maturity assessment Step 4: GPT-4o analyzes product language: aspirational vs. problem-solution fit Step 5: Count and classify integrations as ecosystem strength signal Step 6: Score product-market fit: 0-100 across pricing, ecosystem, proof, depth Step 7: Compare against IAB category peers from domain DB
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Example Output
MCP RESPONSE — product_market_fit_analyzer ════════════════════════════════════════════════════════════ stripebanking.com | Category: IAB13 — Financial Services PRODUCT-MARKET FIT SCORE: 84/100 PRICING TIER ANALYSIS: Tiers detected: 3 (Starter, Growth, Enterprise) Starter: $0/mo (strong freemium on-ramp) Growth: $25/mo (clear value step-up with 15 added features) Enterprise: Custom ("Contact Sales" — annual contract signal) Tier structure score: 88/100 — Well-segmented, clear upgrade path INTEGRATION ECOSYSTEM: Integrations listed: 347 Categories: Accounting (42), CRM (38), E-commerce (67), ERP (28) Notable: Salesforce, QuickBooks, Shopify, SAP connectors Ecosystem score: 91/100 — Deep ecosystem signals strong PMF SOCIAL PROOF SIGNALS: Customer logos: 124 logos displayed across site Testimonials: 18 named testimonials with photos and titles Case studies: 12 detailed case studies with metrics Social proof score: 87/100 — Strong credibility indicators FEATURE DEPTH: Features listed: 67 distinct features across 8 categories Documentation: Comprehensive API docs with 200+ endpoints Changelog: Weekly updates — active development signal Feature depth score: 82/100 — Mature, actively developed product PMF COMPARISON (IAB13 peers, n=127): Percentile rank: 89th percentile Category avg PMF score: 52/100 Top competitor: plaid.com (PMF: 91/100) PMF SIGNALS SUMMARY: Strong freemium-to-enterprise funnel with clear value escalation Deep integration ecosystem indicates sticky product adoption Active social proof and frequent releases confirm market traction

7Pricing Intelligence Gatherer

Scrapes competitor pricing pages to build comprehensive pricing comparison: fee structures, rate sheets, tier breakdowns, and promotional offers. Delivers actionable pricing intelligence for rate-setting and competitive positioning decisions.

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MCP Tool Definition
Web Scraping GPT-4o Vision AI
pricing_intelligence_gatherer competitors: array — List of competitor domains to scrape pricing from product_type: string — "savings","checking","mortgage","credit_card","auto_loan","all" include_promos: boolean — Capture promotional/introductory offers (default: true)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Scrape /pricing, /rates, /fees, /products, /promotions for each competitor Step 2: Screenshot pricing tables and rate comparison pages Step 3: GPT-4o + Vision AI extract structured pricing: APY, APR, fees, minimums Step 4: Normalize pricing data across competitors for direct comparison Step 5: Detect promotional offers: intro rates, bonus offers, waived fees Step 6: Calculate competitive position: above/below/at market for each metric Step 7: Generate pricing comparison matrix with strategic recommendations
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Example Output
MCP RESPONSE — pricing_intelligence_gatherer ════════════════════════════════════════════════════════════ PRODUCT: Savings Accounts | Competitors: 8 banks SAVINGS RATE COMPARISON: APY Minimum Monthly Fee Online Promo ally.com 4.20% $0 $0 Yes — marcusbank.com 4.00% $0 $0 Yes — capitalone.com 3.80% $0 $0 Yes — discover.com 3.75% $0 $0 Yes — chase.com 0.01% $300 $5 No $300 bonus bankofamerica.com 0.01% $100 $8 No $200 bonus wellsfargo.com 0.01% $25 $5 No — YOUR BANK 0.05% $500 $12 No — CHECKING FEE COMPARISON: Market avg monthly fee: $4.75 Online-only avg: $0.00 Traditional avg: $7.50 Your bank: $12.00 — highest in competitive set PROMOTIONAL OFFERS DETECTED: chase.com: $300 new checking + $200 new savings bonus bankofamerica.com: $200 checking bonus (direct deposit required) citibank.com: $2,000 bonus on $300K+ deposit (tiered) YOUR BANK: No promotional offers currently active STRATEGIC RECOMMENDATIONS: 1. Savings APY 83x below online bank average — losing rate-sensitive deposits 2. Monthly fee is highest in set — consider fee-free tier to compete 3. No promotional offers — competitors using $200-$500 bonuses for acquisition 4. Opportunity: launch online-only high-yield savings at 3.50%+ APY

8Content Marketing Effectiveness Scorer

Analyzes /blog content quality, posting frequency, topic coverage, and SEO signals using AI to score content marketing effectiveness. Benchmarks a bank's content strategy against competitors and identifies content gaps and opportunities.

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MCP Tool Definition
Web Scraping GPT-4o Domain DB
content_marketing_scorer domain: string — Target domain to analyze content marketing compare_to: array — Competitor domains for benchmarking max_posts: integer — Maximum blog posts to analyze (default: 50) include_seo: boolean — Include SEO signal analysis (default: true)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Scrape /blog, /resources, /insights, /news for target and competitors Step 2: Extract post titles, dates, word counts, categories, author info Step 3: Calculate publishing frequency, consistency, and recency Step 4: GPT-4o evaluates content quality: depth, originality, readability per post Step 5: Analyze SEO signals: title tags, meta descriptions, heading structure, internal links Step 6: Map topic coverage against banking content taxonomy (20 core topics) Step 7: Score overall content effectiveness and benchmark against competitors
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Example Output
MCP RESPONSE — content_marketing_scorer ════════════════════════════════════════════════════════════ firstnationalbank.com | Posts analyzed: 34 | Competitors: 4 CONTENT EFFECTIVENESS SCORE: 38/100 PUBLISHING FREQUENCY: Posts per month (6-mo avg): 1.8 posts/month Competitor avg: 8.4 posts/month nerdwallet.com: 47 posts/month (content leader) chase.com: 12 posts/month Frequency score: 22/100 CONTENT QUALITY (AI-assessed): Avg word count: 420 words (competitor avg: 1,240 words) Depth score: 28/100 — Surface-level, no data or original research Readability: 72/100 — Clear language, appropriate grade level Originality: 18/100 — Generic advice, no unique insights Quality score: 31/100 TOPIC COVERAGE (20 core banking topics): Covered: 6 of 20 topics (30%) Covered: Savings tips, budgeting, credit scores, mortgage basics, fraud prevention, retirement Missing: Investing, business banking, crypto, digital payments, insurance, financial planning, tax strategy, student loans, home equity, auto finance, estate planning, small business, credit cards, economic outlook SEO SIGNALS: Meta descriptions: 12 of 34 posts missing meta descriptions H1 tags: All present but not keyword-optimized Internal linking: Avg 0.8 internal links per post (best practice: 5+) SEO score: 29/100 COMPETITOR BENCHMARK: Frequency Quality Topics SEO Overall nerdwallet.com 95 88 95 92 92 chase.com 72 74 80 78 76 bankrate.com 89 82 90 88 87 firstnationalbank.com 22 31 30 29 38 RECOMMENDATIONS: 1. Increase publishing to minimum 4x/week to compete 2. Add 14 missing topic categories — start with investing and business banking 3. Increase post depth to 1,000+ words with data and examples 4. Quick win: add meta descriptions to 12 posts missing them

9Social Proof & Credibility Assessor

Scrapes /case-studies, /testimonials, and /customers pages to assess social proof strength: customer logos, success stories, industry awards, certifications, and media mentions. Quantifies trust signals that drive conversion and credibility.

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MCP Tool Definition
Web Scraping GPT-4o Vision AI Domain DB
social_proof_assessor domain: string — Target domain to assess social proof proof_types: array — ["logos","testimonials","case_studies","awards","media","certifications"] compare_to: array — Competitor domains for relative assessment
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Scrape /case-studies, /testimonials, /customers, /awards, /press, homepage Step 2: Screenshot logo grids and testimonial sections for visual analysis Step 3: Vision AI counts and identifies customer logos from screenshots Step 4: GPT-4o extracts testimonial quotes, names, titles, companies Step 5: Evaluate case study quality: metrics-backed, named, detailed vs. vague Step 6: Detect awards, certifications, media mentions as credibility signals Step 7: Score each proof type and calculate composite credibility score
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Example Output
MCP RESPONSE — social_proof_assessor ════════════════════════════════════════════════════════════ midwestcreditunion.com | Proof types: all 6 SOCIAL PROOF SCORE: 42/100 CUSTOMER LOGOS: 12/100 Logos found: 0 — No customer logo section detected Homepage mentions: "Trusted by thousands" — no specifics Competitor avg: 47 logos displayed TESTIMONIALS: 38/100 Testimonials found: 6 Named with photo: 2 of 6 (low credibility — 4 are "John D." style) With title/company: 0 of 6 (no professional context) Quality: Generic praise — "great service", "recommend to friends" CASE STUDIES: 8/100 Case studies found: 0 — No /case-studies page exists Success stories: 1 brief mention in /about page Metrics-backed: None — no quantified outcomes AWARDS & CERTIFICATIONS: 68/100 Awards detected: J.D. Power award badge (2025) Certifications: NCUA insured, Equal Housing Lender Industry memberships: CUNA member, local chamber of commerce MEDIA MENTIONS: 34/100 Press page: Exists but last updated 8 months ago Media logos: None displayed "As seen in": Not present COMPETITOR COMPARISON: Logos Testi Cases Awards Media Overall chase.com 89 78 82 95 91 87 ally.com 72 84 71 78 82 77 capitalone.com 81 69 74 88 87 80 midwestcreditunion.com 12 38 8 68 34 42 RECOMMENDATIONS: 1. Create /case-studies page with 5+ named customer success stories 2. Add customer logo grid to homepage — collect permission from top clients 3. Upgrade testimonials: add full names, photos, titles, and specific outcomes 4. Leverage J.D. Power award more prominently — currently only in footer

10Industry Trend Report Generator

Aggregates signals across an IAB category from the domain database: new domain registrations, PageRank distribution changes, and content trend analysis via AI. Produces a comprehensive industry trend report for strategic planning and market positioning.

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MCP Tool Definition
Domain DB Web Scraping GPT-4o All MCP Services
industry_trend_report_generator iab_category: string — IAB category to generate trend report for country: string — ISO country code for geographic focus (default: "US") lookback_months: integer — Months of historical data to analyze (default: 12) sample_size: integer — Number of top domains to deep-scrape for content trends (default: 100)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Query 100M domain DB for all domains in IAB category + country Step 2: Analyze domain age distribution to detect new entrant wave patterns Step 3: Track PageRank distribution changes over lookback period Step 4: Sample top N domains and scrape /blog, /products, /press for content trends Step 5: GPT-4o identifies emerging topics, buzzwords, product innovation patterns Step 6: Cross-reference with hiring trends, pricing shifts, and brand positioning data Step 7: Synthesize all signals into structured industry trend report with forecasts
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Example Output

Industry Trend Report — IAB13 Personal Finance, US Market, Q1 2026

EXECUTIVE SUMMARY ──────────────────────────────────────── Category: IAB13 — Personal Finance | Country: US | Lookback: 12 months Domains analyzed: 14,872 | Deep-scraped sample: 100 | Signals aggregated: 47,219 MARKET DYNAMICS ──────────────────────────────────────── Total domains in category: 14,872 (+8.4% YoY) New registrations (12 months): 1,247 new domains Domains gone offline: 412 domains (2.8% attrition) Net growth: +835 domains (+5.6% net) PAGERANK DISTRIBUTION SHIFT ──────────────────────────────────────── PR 8-10 (Market Leaders): 127 → 131 (+3.1%) — Consolidation at top PR 5-7 (Established): 2,341 → 2,518 (+7.6%) — Mid-market growing PR 3-5 (Growing): 5,847 → 6,102 (+4.4%) — Steady expansion PR 1-3 (Emerging): 6,557 → 6,121 (-6.6%) — Washout of weak entrants Key insight: Market is bifurcating — leaders consolidate, weak entrants exit TOP CONTENT TRENDS (from 100 sample domains) ──────────────────────────────────────── 1. AI-Powered Financial Planning — Mentioned by 67% of sample Trend strength: ████████████████████ STRONG 12-month growth in mentions: +340% Key domains: wealthfront.com, betterment.com, sofi.com 2. Embedded Finance / BaaS — Mentioned by 42% of sample Trend strength: ████████████████░░░░ STRONG 12-month growth: +215% Key domains: stripe.com, marqeta.com, synapse.fi 3. Real-Time Payments — Mentioned by 38% of sample Trend strength: ████████████░░░░░░░░ MODERATE 12-month growth: +167% Key domains: zellepay.com, venmo.com, wise.com 4. Open Banking / API Economy — Mentioned by 31% of sample Trend strength: ██████████░░░░░░░░░░ MODERATE 12-month growth: +89% Key domains: plaid.com, mx.com, finicity.com 5. Crypto / DeFi Banking — Mentioned by 14% of sample Trend strength: ██████░░░░░░░░░░░░░░ DECLINING 12-month growth: -42% Note: Multiple domains removed crypto content in last 6 months COMPETITIVE LANDSCAPE SHIFTS ──────────────────────────────────────── Biggest gainers (PageRank increase): 1. mercury.com — PR: 4.1 → 6.3 (+2.2) — Business banking surge 2. ramp.com — PR: 3.8 → 5.9 (+2.1) — Corporate card expansion 3. brex.com — PR: 5.2 → 6.8 (+1.6) — Enterprise pivot success Biggest decliners (PageRank decrease): 1. ftx.com — PR: 7.1 → 1.2 (-5.9) — Collapse 2. silvergate.com — PR: 5.4 → 0.8 (-4.6) — Shutdown 3. cellsius.network — PR: 4.8 → 1.1 (-3.7) — Bankruptcy HIRING TREND SIGNALS (from careers pages) ──────────────────────────────────────── Fastest growing roles: AI/ML Engineers (+187%), Compliance (+94%), Product (+67%) Declining roles: Branch staff (-34%), Tellers (-52%), Print marketing (-78%) Geographic shift: Remote roles up +124%, physical branch roles down -41% PRICING TREND SIGNALS ──────────────────────────────────────── Savings rates: Average APY increased from 3.2% to 4.1% (+0.9%) Fee pressure: 8 of top 20 banks eliminated monthly checking fees in 12 months Free tier growth: 62% of fintechs now offer free base tier (was 41%) 12-MONTH FORECAST ──────────────────────────────────────── 1. AI integration will become table stakes — banks without AI tools will lose market share 2. Embedded finance will drive 200+ new entrants in next 12 months 3. Fee compression will accelerate — expect 3-5 more major banks to go fee-free 4. Crypto-adjacent banking will continue to contract as regulatory clarity stalls 5. Business banking neobanks (Mercury, Ramp, Brex) will challenge traditional commercial banks STRATEGIC IMPLICATIONS FOR YOUR BANK ──────────────────────────────────────── Urgent: Develop AI-powered advisory tools — 67% of competitors already messaging this Important: Evaluate embedded finance partnerships before market saturates Opportunity: Real-time payments integration as differentiator for business clients Defensive: Re-evaluate fee structure — market is rapidly moving to fee-free models
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