mcp-bizintel

BizIntel MCP — Local Business Intelligence for AI Agents

Real-time website audits, lead scoring, tech-stack detection, and local-business search — exposed as an MCP server. Built for AI agents doing sales outreach, competitor research, and prospecting at scale.

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MCPize Listing Copy

Title: BizIntel MCP — Real-Time Local Business Intelligence

Subtitle: Audit any website, score leads, find businesses with no booking system. Pay per call.

Description (2 paragraphs):

BizIntel is a paid MCP server that gives AI agents instant access to the kind of local-business intelligence sales teams used to pay analysts to gather. One call to audit_website returns a 0-100 score across SSL, mobile-readiness, page speed, contact form presence, and online booking — plus a tech-stack fingerprint (CMS, booking platform, email provider, analytics). Agents pointed at "find dentists in Austin with no booking system" get a ranked, contactable list in seconds, not hours.

The MCP exposes eight tools — audit_website, bulk_audit, search_businesses, get_business_details, score_lead, find_no_website, find_no_booking, get_tech_stack — backed by Yelp Fusion (or OSM/Overpass when no Yelp key is provided), an aiohttp scanner running 10 concurrent fetches, and a 24-hour SQLite cache so repeat calls don't burn quota. Drop it into Claude Desktop, Cursor, or any agent and your prospecting pipeline becomes one tool call wide.

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Pricing

| Tier | Price | Limits | |------|-------|--------| | Free / Dev | $0 | 20 calls / 24h | | Pro | $19/mo | Unlimited | | Pay-as-you-go | $0.05/call | No floor |

Upgrade: <https://mcpize.com/bizintel-mcp>

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Tools

| Tool | Args | Returns | |------|------|---------| | audit_website | url | Score 0-100, SSL, HTTPS redirect, viewport, load_time_ms, contact form, booking, tech_stack | | search_businesses | niche, city, state, limit | List of normalized business records | | get_business_details | business_name, city | Full record: phone, address, website, hours, rating, lat/lon | | bulk_audit | urls (≤20) | Audit results sorted worst→best (best leads first) | | score_lead | business_name, city, niche | Composite 0-100 lead score with breakdown | | find_no_website | niche, city, state, limit | Hottest cold-outreach leads — ranked | | find_no_booking | niche, city, state, limit | Have a site, no online booking — SaaS-pitch ready | | get_tech_stack | url | CMS / booking / email / analytics fingerprint |

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Quickstart

Add to Claude Desktop / Claude Code

claude mcp add bizintel-mcp --url https://mcp-bizintel.up.railway.app/mcp

Set your API key in the MCP config (header X-API-Key). The default dev key bizintel-dev-key-001 is good for 20 calls per day.

Direct HTTP

curl -X POST https://mcp-bizintel.up.railway.app/v1/find_no_booking \
  -H "X-API-Key: bizintel-dev-key-001" \
  -H "Content-Type: application/json" \
  -d '{"niche":"dentist","city":"Austin","state":"TX","limit":10}'

Example agent prompt

Find dentists in Austin with no booking system, audit the top 5, and write a one-line cold-email opener for each that references their actual tech stack.

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Local Dev

pip install -r requirements.txt
cp .env.example .env  # fill in YELP_API_KEY (optional)
python -m uvicorn server:app --reload --port 8000
pytest -v

Without a Yelp key, search/details fall back to OSM (Nominatim + Overpass). Coverage is sparser but free.

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Deploy

bash deploy.sh from Brett's Mac — pulls secrets from the shared workspace .deploy-secrets.env, links/initializes the Railway project, sets env vars, and runs railway up. Pre-deploy pytest is part of the script so you can't ship a broken build.

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Architecture

server.py                FastAPI + fastmcp; HTTP at /v1/* and MCP at /mcp
tools/audit.py           Async aiohttp auditor (10 concurrent, 5s timeout)
tools/techstack.py       CMS / booking / email / analytics fingerprints
tools/search.py          Yelp Fusion primary, OSM Overpass fallback
tools/scoring.py         Composite lead score (0-100)
db/cache.py              SQLite cache + per-key call ledger
db/keys.py               Tier classification + 24h sliding window
nixpacks.toml            Railway build config

Caching: audits cached 6h, business details 24h, search results 12h. The cache is a single SQLite file with WAL mode — no Redis needed for the price point.

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Known Limitations

1. Yelp doesn't expose external website URL in /businesses/search. We return yelp_url as a stable handle; for a real domain, agents should chain get_business_details (returns hours/photos) and follow the Yelp page or use find_no_website (where OSM-tagged sites are surfaced).
2. OSM coverage is uneven — some niches map cleanly (dentist, restaurant); long-tail US small business categories (pickleball coach, yacht detailer) won't resolve.
3. No headless rendering — JS-heavy sites that gate content behind hydration won't expose contact/booking signals to the audit. This is intentional; we trade completeness for 5-second batched audits.
4. Tech-stack detection is signature-based, not Wappalyzer-grade. We catch the common 90% (WP, Wix, Shopify, Squarespace, Calendly, Mindbody, GA4, Klaviyo) — not obscure custom stacks.
5. Rate-limit window is sliding 24h, stored per-API-key in SQLite. Restart the container and the ledger persists; clear the DB to reset all dev quotas.

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License

Proprietary — © 2026.