BizIntel MCP
The server detects Calendly as a booking platform on websites, providing insights into online scheduling capabilities for lead scoring.
The server detects Shopify as a CMS/e-commerce platform on websites, contributing to tech-stack fingerprinting for competitive analysis.
The server detects Squarespace as a CMS on websites, aiding in tech-stack identification for sales outreach.
The server detects Wix as a website builder/CMS on websites, used in tech-stack detection to inform lead scoring.
The server integrates with Yelp Fusion API to search for businesses, retrieve details, and generate leads for local business intelligence.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@BizIntel MCPfind dentists in Austin with no booking system"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
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.
Related MCP server: techstack-detective-mcp
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
Tools
Tool | Args | Returns |
|
| Score 0-100, SSL, HTTPS redirect, viewport, load_time_ms, contact form, booking, tech_stack |
|
| List of normalized business records |
|
| Full record: phone, address, website, hours, rating, lat/lon |
|
| Audit results sorted worst→best (best leads first) |
|
| Composite 0-100 lead score with breakdown |
|
| Hottest cold-outreach leads — ranked |
|
| Have a site, no online booking — SaaS-pitch ready |
|
| CMS / booking / email / analytics fingerprint |
Quickstart
Add to Claude Desktop / Claude Code
claude mcp add bizintel-mcp --url https://mcp-bizintel.up.railway.app/mcpSet 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.
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 -vWithout a Yelp key, search/details fall back to OSM (Nominatim + Overpass). Coverage is sparser but free.
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.
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 configCaching: 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.
Known Limitations
Yelp doesn't expose external website URL in
/businesses/search. We returnyelp_urlas a stable handle; for a real domain, agents should chainget_business_details(returns hours/photos) and follow the Yelp page or usefind_no_website(where OSM-tagged sites are surfaced).OSM coverage is uneven — some niches map cleanly (
dentist,restaurant); long-tail US small business categories (pickleball coach,yacht detailer) won't resolve.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.
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.
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.
License
Proprietary — © 2026.
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