Fireshift Leak Score

Scan a website's Google Ads and GA4 conversions

scan_site

Read-onlyIdempotent

Reads the public homepage of a domain, finds its Google Tag Manager and Google tag container IDs, loads those containers from googletagmanager.com and scores the conversion setup from 0 to 100 (grade A to F). Findings include Google Ads conversions that fire on every page load, on plain page views, on login, on button clicks or when a trial ends, GA4 key events on page_view, duplicate conversions, conversions without a value, and more than one GA4 property. Same checks and score as https://fireshift.ai/scan/. Reads only what the site serves publicly; no login, no forms. It cannot see whether an Ads conversion is primary or secondary. Tags loaded server-side or only after cookie consent are not visible, so a site with no public container gets no score. Takes 2 to 20 seconds.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "domain": "<domain>"
    },
    "name": "scan_site"
  }
}

Input Schema

Name	Required	Description	Default
`domain`	Yes	Domain to scan, e.g. example.com (no path, no IP).

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 11, 2026

TDQS

A4/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, idempotent, non-destructive and open-world behavior, yet the description still adds substantive context: it states it reads only publicly served content, requires no login or forms, and takes 2 to 20 seconds. It also discloses its blind spots, including inability to see primary versus secondary conversions and server-side or consent-gated tags, though it omits any mention of caching or repeat-scan behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with purpose and score, then disclaims limitations before the runtime caveat. The middle inventory of findings runs long, but each item is concrete and earns its place by telling the agent what a scan reveals.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter scan with no output schema, the definition covers mechanism, output shape, score range, limitations, and latency. An agent has everything needed to invoke it and set expectations about results and runtime.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for the single domain parameter, and the schema already specifies 'no path, no IP.' The description adds no format or validation detail beyond prose mention of 'a domain,' so the schema carries the parameter semantics and baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb-plus-resource chain: reads a domain's public homepage, extracts GTM and Google tag container IDs, loads them from googletagmanager.com, and scores conversion setup 0-100 with grades A-F. It lists the concrete findings, so an agent knows exactly what the tool produces and how it differs from a generic stats tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the described mechanism rather than stated: you scan a domain to audit its conversion setup, and the description notes the score mirrors fireshift.ai/scan/. It never names the sibling get_stats or says when scanning is preferable to alternatives, so the agent must infer routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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