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

Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the bar is lower. The description still adds real behavioral context: the data comes from saved HTTP checks with no live store requests or model calls, and unknown/not-checked values are kept separate.

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?

Front-loaded with the purpose, but the single semicolon-chained sentence packs scope, methodology, an odd 'shopper approval required' clause, and provenance into one dense run-on, which hurts scannability without being truly verbose.

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, the description usefully characterizes what is returned (adoption counts, category and platform rates, Checkout WebMCP among Shopify checkouts, separate unknown/not-checked buckets) and sets scope limits, which is enough for a zero-parameter read tool.

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?

The tool takes zero parameters and schema coverage is 100%, so there is nothing for the description to disambiguate. Baseline 4 applies with no gap to compensate for.

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 (Read) and a clear resource (ARC's agent commerce adoption counts, category and commerce platform rates) plus the data source (saved HTTP checks). It is distinguishable from siblings like google_checkout_readiness or product_data_check, though it never names them to sharpen the boundary.

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?

Usage is implied rather than stated: the scope caveats ('Curated sample, not all e-commerce', 'No store requests or model calls') tell the agent what the data does and does not cover, but there is no explicit when-to-use-this-vs-alternative routing among the many siblings.

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