marketing-page-quality-gate
Provides tools for retrieving campaign metrics (mocked) and reading ad spend CSV files from Google Ads, enabling spend gating decisions.
Provides tools for retrieving campaign metrics (mocked) and reading ad spend CSV files from Meta Ads Manager, enabling spend gating decisions.
Provides tools for retrieving campaign metrics (mocked) and reading ad spend CSV files from TikTok Ads Manager, enabling spend gating decisions.
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Here is a step-by-step guide with screenshots.
Marketing Page Quality Gate — MCP Server
Stop paying to send traffic to leaky pages. AI velocity never ships a leaky funnel.
A media-buying team ships AI-generated landing pages at velocity. The expensive failure mode isn't ugly copy — it's a leaky funnel silently burning ad spend: a missing Meta Pixel so you can't retarget or even measure, a CTA buried below the fold, a fixed-width shell that horizontal-scrolls on every phone, a 1.5MB page that bounces before it paints, a dead link in the hero. None of it shows up in a glance. Every one of them wastes budget.
This is an MCP server that scores any landing page deterministically — the same page always gets the same grade — and translates each defect into dollars of ad spend at risk, so a page gets gated before a dollar of budget hits it. CodeSieve-for-marketing-pages.
It does two things:
Quality Gate (real, working core). Score a page A–F on the signals that move conversion, get the % of ad spend at risk, and block spend on sub-C pages — all deterministic, no API keys, offline.
Ad-platform connector (Half-A). Campaign metrics for Meta / Google / Taboola / TikTok — mocked demo data (
"mock": true), plus a real CSV-export reader so the spend gate runs on your actual numbers today. Live API keys are a documented TODO.
See it on real pages
We graded 7 recognizable DTC landing pages as-is (Dr. Squatch, HelloFresh, Bombas, AG1, Magic Spoon, Ruggable, Manscaped). Not one scored an A — even big brands ship leaky funnels.

Regenerate it yourself: PYTHONPATH=. uv run python examples/build_scorecard.py → report/scorecard.html.
▶ 90-second demo video: demo/demo_video.mp4 — the gate scoring a leaky page
F, a clean page A, blocking $10,711 of spend, and the live scorecard. (Script: demo/VIDEO_SCRIPT.md.)
Related MCP server: supply-ops-agent
The tools (8)
Tool | What it returns |
| A–F grade + per-signal scores + % spend at risk |
| PASS/BLOCK verdict + $ at risk — refuses to greenlight a sub-C page |
| Meta Pixel / GA4 / GTM / TikTok presence |
| Viewport, responsiveness, horizontal-scroll risk |
| HTML weight, render-blocking scripts, layout-shift images |
| Primary CTA presence, count, above-the-fold |
| Broken / internal / outbound links with HTTP status codes |
| MOCKED spend / ROAS / creative perf ( |
Page tools take either a live url (fetched server-side) or raw html — so you score AI
output before it's ever deployed.
Sample score_page output
{
"grade": "B",
"score": 85,
"signals": { "pixels": 0, "mobile": 100, "cta": 100, "speed": 100, "links": 100 },
"spend_at_risk": {
"risk_pct": 30,
"factors": ["Missing tracking pixels blind optimization"]
}
}The grade
Five conversion signals, weights sum to 1.0 (links .25, mobile .20, cta .20, speed .20, pixels .15), mapped to a letter: A≥90 · B≥80 · C≥70 · D≥60 · else F.
Spend at risk (think like a buyer, not a QA engineer)
The dollar lens differs from the quality lens. A missing pixel is the single biggest dollar
leak — with no pixel you can't measure conversions, can't optimize, can't build retargeting
audiences: the entire budget runs blind. So in spend_at_risk, pixels rank top
(pixels 30, links 25, cta 20, mobile 15, speed 10). gate_spend multiplies that risk by your
real CSV spend and blocks any page below a C.
Run it
Requires Python 3.14 and uv.
uv sync # install deps (mcp, beautifulsoup4, httpx)
uv run pytest -q # 54/54 — the deterministic oracle
uv run python -m quality_gate.server # start the MCP server (stdio)A no-MCP taste of the scoring: PYTHONPATH=. uv run python demo/show.py score fixtures/leaky_funnel.html.
Register with an MCP client
Claude Code (one-liner):
claude mcp add quality-gate -- uv --directory /path/to/itstoday-entry run python -m quality_gate.serverClaude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):
{
"mcpServers": {
"marketing-page-quality-gate": {
"command": "uv",
"args": ["--directory", "/path/to/itstoday-entry", "run", "python", "-m", "quality_gate.server"]
}
}
}Then, in natural language: "Gate spend on this landing page before we launch it" → the agent
calls gate_spend and gets a PASS/BLOCK verdict with the dollars at risk.
Scope & limits (read this — it's a strength)
This is deterministic static analysis of the pre-launch HTML you control, before you spend. It's built for the buyer's real page type — server-rendered campaign / DTC / affiliate landing pages (Shopify, ClickFunnels, Leadpages, Carrd, WordPress) where the pixel, CTA, and content are in the HTML payload, exactly what static analysis reads accurately and what a CI/agent step can gate in milliseconds with no browser.
It is not a JS-SPA homepage auditor. A React/Next homepage (e.g. a big SaaS marketing site) injects its pixels and renders its CTAs client-side after JS runs, so static analysis of the served HTML will under-report them. That's a deliberate boundary, not a miss: a pixel that only fires after a heavy JS bundle genuinely misses fast-bounce conversions — and the page you're about to gate in your pipeline is your own pre-launch HTML, which is static by definition.
What's real vs. mocked (honest)
Quality Gate — real, working, deterministic. No external keys. This is the demo.
Ad connectors —
get_campaign_metricsis mocked ("mock": true, with anotepointing at where real API keys plug intoconnectors.py).read_spend_csvis real — it parses an actual Meta/Google/Taboola Ads Manager CSV export sogate_spendworks on live spend today. Nothing pretends to be a live API it isn't.
How it was built — the Grok contract-loop
Built with a deterministic contract loop: Claude authored a pytest oracle
(contract/pytest_oracle.py, 54 criteria) testing every scorer against fixture HTML pages with
known defects; Grok built the implementation under quality_gate/ until the oracle went green,
one passing criterion at a time (build_loop.sh). Grok never grades itself — the oracle does,
and the contract (tests/, fixtures/, CONTRACT.md) is protected from the builder. A 3-agent
review then found real bugs (CTA substring false-matches, pixel-id false positives, a
protocol-relative-URL crash, a max-width scroll-risk false positive) — each was fixed the same
way: write a failing guard test first, then let the loop fix it against the oracle.
contract/pytest_oracle.py # the deterministic oracle (Claude-authored)
tests/ # 54 acceptance tests = the contract
fixtures/ # HTML pages with known defects
examples/ # illustrative pages + the live scorecard generator
CONTRACT.md # exact spec Grok built against
build_loop.sh # the Grok contract-loop
quality_gate/ # the implementation (Grok-built)
pixels · mobile · cta · speed · links · scoring · connectors · gate · serverBuilt for the It's Today Media build challenge · Marcin Dudek · marcin.dudek.dev@gmail.com
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