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GAIP Listing Observer

Does a shop listing agree across its sources? (preview)

gaip_observe_listing
Read-onlyIdempotent

Use this before recommending or buying from an online shop: do the listing's price, currency, stock and SKU agree across the page's structured data, meta tags and platform data? Pass the page url. Preview: only fields where all sources agreed are reported. Not Amazon, eBay or Etsy. Free, read-only, no account; inputs must be public and non-personal.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesPublic https link to one product page, e.g. https://shop.example/products/linen-shirt
data_classificationNoPUBLIC (default) or NON_PERSONAL_PUBLIC. Never send personal data.PUBLIC

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover safety (readOnlyHint, idempotentHint, destructiveHint=false, openWorldHint), so the bar is lower. The description still adds real behavioral context: preview-mode output scope, free/no-account, and that inputs must be public and non-personal. It does not describe failure modes (e.g. what happens when sources disagree or the page lacks structured data), which is the remaining gap.

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?

Compact and front-loaded with the purpose before constraints and exclusions. Slightly clause-dense (constraints and exclusions packed into one run-on final sentence), but every sentence contributes routing or constraint information rather than filler.

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?

With no output schema, the description must convey what comes back, and it does: agreement is reported only for fields where all sources matched. Combined with the input rules and domain exclusions, an agent can call this correctly; only disagreement/error behavior remains undocumented.

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% and both parameters are fully documented, so the schema carries the load. The description's 'Pass the page url' and 'inputs must be public and non-personal' merely restate what the url and data_classification schema descriptions already say, adding no new syntax or format detail.

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 names a concrete operation (cross-checking price, currency, stock and SKU across structured data, meta tags and platform data) on a named resource (shop listing page). It goes beyond the title by enumerating the exact fields being reconciled, so an agent knows precisely what the tool verifies. No siblings exist, so differentiation is moot.

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

Usage Guidelines5/5

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

States an explicit when ('before recommending or buying from an online shop') and explicit when-nots ('Not Amazon, eBay or Etsy'), plus the preview limitation that only agreed fields are reported. This is exactly the routing information an agent needs before choosing the tool.

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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