Competitive Digital Intelligence MCP

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Quick Start

Add to your MCP client (Claude Desktop, Cursor, Windsurf):

{
  "mcpServers": {
    "competitive-digital-intelligence-mcp": {
      "url": "https://ryanclinton--competitive-digital-intelligence-mcp.apify.actor/mcp"
    }
  }
}

---

Competitive digital intelligence for AI agents — this MCP server delivers a full-spectrum analysis of any competitor's digital presence by orchestrating 8 specialist data sources in parallel. It is built for strategy teams, product managers, and AI-powered research workflows that need structured, scored intelligence rather than raw scraped data.

Every tool call returns not just raw data but a scored result: a 0-100 Competitive Intelligence Score built from four weighted dimensions covering technology sophistication, search dominance, e-commerce strength, and brand reputation. Feed the output directly into your AI agent's context window and ask it to generate strategy recommendations.

What data can you extract?

| Data Point | Source | Example |
|-----------|--------|---------|
| 📡 Technology stack (frameworks, CDNs, analytics, payment processors) | Website Tech Stack Detector | React 18, Next.js, Cloudflare, Stripe |
| 🔍 SERP keyword rankings and positions | SERP Rank Tracker | 14 keywords in top 10, 3 in top 3 |
| 🛒 Shopify product catalog size and collections | Shopify Store Intelligence | 347 products, 12 collections |
| 💰 Product pricing and active discount counts | E-Commerce Price Monitor | 28 discounted SKUs, avg $47.20 |
| ⭐ Cross-platform review ratings and sentiment | Multi-Review Analyzer | 4.2/5 avg across 3 platforms |
| 📋 Trustpilot TrustScore and review volume | Trustpilot Review Analyzer | TrustScore 4.4, 1,840 reviews |
| 🕰 Wayback Machine snapshot history | Wayback Machine Search | 89 snapshots since 2017 |
| ♿ WCAG accessibility violations by severity | WCAG Accessibility Auditor | 3 critical, 7 serious violations |
| 🏆 Composite Competitive Intelligence Score | Scoring engine | 68/100 — STRONG verdict |
| 📋 AI-generated strategic recommendations | Scoring engine | 5 actionable competitive signals |

Why use Competitive Digital Intelligence MCP?

Building a competitive analysis by hand means opening 8 different tools, exporting 8 different CSVs, and spending half a day normalizing data before you can say anything meaningful. By the time the slide deck is done, the data is stale.

This MCP server automates the entire pipeline. A single tool call fires all 8 data sources in parallel, scores each dimension against a calibrated 0-100 model, and returns a structured JSON report with verdict and strategic recommendations — in under two minutes.

- Scheduling — run weekly competitive sweeps automatically to track score changes over time
- API access — call from any AI agent, Python workflow, or HTTP client with one request
- Parallel execution — all 8 data sources run simultaneously via Promise.all, not sequentially
- MCP-native — works with Claude Desktop, Cursor, Windsurf, Cline, and any MCP-compatible client
- Spending controls — each tool enforces per-event charge limits so your agent cannot overspend

Features

- 8-source parallel orchestration — fires website-tech-stack-detector, serp-rank-tracker, shopify-store-intelligence, ecommerce-price-monitor, multi-review-analyzer, trustpilot-review-analyzer, wayback-machine-search, and wcag-accessibility-auditor simultaneously via Promise.all
- Tech stack sophistication scoring — compares detected technologies against a curated MODERN list (React, Next.js, Vue, Nuxt, Svelte, Angular, TypeScript, GraphQL, Tailwind, Vercel, Cloudflare, AWS, GCP, Stripe) and LEGACY list (jQuery, Flash, ASP.NET, ColdFusion, Dreamweaver, FrontPage) to produce a score from 0-100
- Four-tier score breakdown — each analysis returns a sub-score (tech stack 0-100, SEO 0-100, e-commerce 0-100, reputation 0-100) plus a composite weighted score
- Composite weighting formula — SEO 30% + e-commerce 25% + reputation 25% + tech stack 20%, calibrated to reflect market impact
- Five-level verdict labels — WEAK_COMPETITOR, EMERGING, ESTABLISHED, STRONG, MARKET_LEADER for instant executive communication
- WCAG-as-SEO signal — accessibility violations (critical/serious) are factored into the SEO score because Core Web Vitals and accessibility correlate with search rankings
- Wayback evolution scoring — snapshot count (20+, 50+ thresholds) contributes to tech stack history score, capturing web longevity and investment continuity
- Actionable recommendations — five conditional recommendation strings generated automatically based on detected patterns (legacy tech, dominant SERP, large catalog, excellent reputation)
- Granular price competitiveness signal — counts discounted SKUs from the price monitor and flags aggressive pricing strategies when 5+ products carry discounts
- Standalone focused tools — six targeted tools (tech_stack_analysis, seo_ranking_intelligence, ecommerce_competitive_analysis, reputation_monitor, price_intelligence, website_evolution_tracker) for partial analyses at lower cost
- Spending limit enforcement — every tool checks chargeResult.eventChargeLimitReached before executing and returns a structured error if the limit is reached, protecting agent budgets

Use cases for competitive digital intelligence

Marketing competitive benchmarking

Strategy teams and CMOs need a defensible number when presenting competitive positioning to leadership. Run full_competitive_audit monthly against your top 3-5 competitors and track the composite score over time. When a competitor's score jumps from 48 to 67 in a quarter, you know they made a significant investment and can investigate where.

Product team technology intelligence

Product managers and engineering leads use tech_stack_analysis to detect when competitors adopt new frameworks or infrastructure. If a competitor migrates from jQuery to React with a Cloudflare CDN layer, that is a signal of technical investment that may precede a UX overhaul or performance push. The Wayback Machine dimension reveals when the migration happened.

E-commerce pricing strategy

Retail analysts and category managers use ecommerce_competitive_analysis and price_intelligence to monitor competitor product breadth and promotional patterns. Knowing that a competitor has 347 products and 28 actively discounted SKUs — versus your 120 products with 4 discounts — informs both assortment and promotional planning.

Investor and M&A digital due diligence

Investment analysts use full_competitive_audit and tech_stack_analysis during target assessment. A LEGACY tech stack verdict with 3+ legacy indicators is a proxy for engineering debt. A Trustpilot score below 3.0 alongside a DOMINANT competitor creates a clear narrative around market risk. The structured JSON output feeds directly into investment memo templates.

Reputation risk monitoring

Brand managers and customer experience leads use reputation_monitor to track review sentiment across Trustpilot and multi-platform sources simultaneously. The scoring model flags when a brand has 5+ negative reviews and when the Trustpilot average diverges significantly from multi-platform data — an early warning signal for reputational divergence.

AI agent competitive research workflows

AI engineers building autonomous research agents embed this MCP to give their agents structured competitive context. An agent tasked with "analyze the top 3 competitors in the CRM market" can call compare_competitors for each one, receive a normalized score with signals, and synthesize a comparative brief without writing any scraping code.

How to connect this MCP server

Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "competitive-digital-intelligence": {
      "url": "https://competitive-digital-intelligence-mcp.apify.actor/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_APIFY_TOKEN"
      }
    }
  }
}

Cursor / Windsurf / Cline

Add the MCP server URL in your IDE's MCP configuration panel:

URL: https://competitive-digital-intelligence-mcp.apify.actor/mcp
Auth: Bearer YOUR_APIFY_TOKEN

Programmatic HTTP (cURL)

```bash