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Audit AI Hallucinations Against Brand Truth Sheet

check_brand_hallucinations

Paid Tool (Pro/Agency): Compares ChatGPT, Gemini, and Perplexity claims against verified pricing, shipping/delivery thresholds, and SKU exclusions. Costs 1 credit cached or 10 credits with live_refresh=true.

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": "check_brand_hallucinations"
  }
}

Input Schema

NameRequiredDescriptionDefault
domainYesTarget domain or URL
live_refreshNo
false_claims_to_flagNo
shipping_or_service_areaNo
verified_pricing_summaryNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.2/5.0
Behavior2/5

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

No annotations are supplied, so the description carries the full behavioral burden. It does disclose a genuinely useful trait – credit cost (1 cached, 10 with live_refresh=true) – but says nothing about what the audit returns, whether it is read-only, what happens on failure, or how long a live refresh takes.

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?

Two tight sentences with no filler; the tool's function is front-loaded after the plan-tier marker. Slightly awkward that the tier/pricing prefix precedes the actual purpose.

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

Completeness3/5

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

For a five-parameter, no-output-schema, no-annotation tool, the description should do more: return shape and half the parameters are left opaque. Cost transparency and the list of claim categories are useful but not sufficient.

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 only 20% (just `domain`), so four parameters are undocumented in the schema. The description partially compensates by tying live_refresh to a 10-credit live-refresh mode and by gesturing at pricing, shipping thresholds, and SKU exclusions, but false_claims_to_flag and shipping_or_service_area remain unexplained.

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

Purpose4/5

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

Names a specific verb (compares) plus the exact resources checked – ChatGPT/Gemini/Perplexity claims versus verified pricing, shipping thresholds, and SKU exclusions. This clearly distinguishes it from sibling tools like scan_domain_ai_readiness or get_citation_gaps_and_fanouts, though it never names an alternative explicitly.

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?

The 'Paid Tool (Pro/Agency)' prefix and credit cost imply the context in which to reach for it (a paid audit of AI claims), but there is no explicit when-to-use versus when-not guidance and no declared alternative among the siblings.

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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