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Run an AI search audit

run_ai_search_audit

AI search readiness audit. Paid service using the public x402 HTTP endpoint. Without payment_signature, returns current payment requirements and does not charge. After operator authorization, a compatible wallet signs those requirements; retry this tool with that base64 PAYMENT-SIGNATURE. Never send a private key. Keep the same URL and signed authorization after an uncertain response; do not create another payment. Read list_services for the current price.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesPublic HTTPS website URL.
payment_signatureNoOptional base64 x402 v2 PAYMENT-SIGNATURE produced by the payer wallet for this service. Omit for a quote.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

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

Adds substantial context beyond the annotations: the service is paid via a public x402 endpoint, an unsigned call is free and returns payment requirements, the wallet-signing step requires operator authorization, private keys must never be sent, and an uncertain response should not trigger a fresh payment. This retry guidance is especially valuable given idempotentHint=false. No contradiction with annotations.

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

Conciseness5/5

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

Five dense sentences cover purpose, payment model, quote behavior, authorization flow, security, and retry/idempotency guidance with zero filler. The core purpose is front-loaded before the payment mechanics.

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

Completeness4/5

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

For a tool with no output schema, the payment/retry flow is exceptionally well covered. The main gap is that the actual audit result is never described, and the format of the 'payment requirements' response is unspecified. Still, the trickiest parts of this unusual monetized tool are fully handled.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3, but the description adds real meaning: it explains the payment_signature lifecycle (omit = free quote, provide = paid execution), clarifies that url must stay stable across the two-phase flow, and warns against sending a private key as the signature. This goes beyond the schema's one-line field descriptions.

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?

The description names the verb+resource clearly: running an 'AI search readiness audit.' The paid x402 endpoint and the 'read list_services for the current price' pointer subtly differentiate it from the free sibling tools, but it never explicitly contrasts with the other run_* audit tools (run_llms_txt_draft, run_structured_data_pack), so an agent must infer the boundary.

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

Usage Guidelines4/5

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

The description gives an explicit two-step invocation pattern (call without payment_signature for a free quote, then retry with the wallet-signed base64 PAYMENT-SIGNATURE) and routes price questions to list_services. It does not state explicit when-not-to-use conditions or contrast with alternatives beyond list_services.

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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