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

No annotations are supplied, so the description carries the full burden and it does meaningful work: it discloses that the source is a public endpoint (implying no auth), that the operation is a read, and exactly which failure classes are surfaced. It omits error behaviour, rate limits, and how sampling affects coverage.

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

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

A single dense sentence with the subject (what is read) front-loaded and the checks enumerated after it; the Shopify-only caveat is placed last where it functions as a scope qualifier. No filler clauses.

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

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

With no output schema and no annotations, the description must stand alone, and it adequately covers source, applicability, and reported findings. The one gap is the absence of any statement about return shape or how the sample limit shapes results, which matters for a sampling-based audit.

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

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

Schema description coverage is 100%, so both url and limit are already documented in the schema; baseline 3 applies. The description adds no further parameter meaning, and notably does not connect the 'sample' semantics of limit to the completeness of the audit report.

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

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

States a specific verb and resource (reads a Shopify store's public products.json) plus the exact defect classes it reports: missing barcodes, absent weights, unpriced/unavailable variants, thin descriptions. The 'Shopify-only' scoping clause cleanly separates it from the generic readiness/structured-data/tracking siblings.

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

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

The description gives a clear applicability rule ('Shopify-only, because products.json is a Shopify endpoint'), which tells the agent when this tool does and does not apply. It stops short of naming an alternative sibling for non-Shopify or non-feed audits, so the routing is inferable rather than explicit.

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