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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive behavior, so the bar is lower. The description adds meaningful context beyond them: the output is a deterministic similarity key rather than a score, and each call costs $0.01 USDC via x402 — a real invocation consideration. Auth mechanics beyond the payment mention are left implicit.

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?

Two tight sentences, front-loaded with the core purpose and mechanism, followed by use cases and cost. No redundant restatement of the name or title.

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?

For a 2-parameter tool with no output schema, the description explains the return concept (a matching key) and the cost model, which is enough to call it correctly. It could say more about how to consume the key (e.g., equality comparison) but nothing essential is missing.

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 coverage is 100%, so both parameters are already documented. The description reinforces what 'product' accepts (name, description, model numbers) but adds nothing about the 'algorithm' variant or when to choose one, so it sits at the baseline.

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 ('generates an AI-powered similarity key') and resource ('a product name or description'), and explains the mechanism (variations normalize to the same key). This clearly distinguishes it from sibling match tools that operate on addresses, companies, and full names.

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?

Gives concrete use cases — matching products across catalogs/marketplaces/supplier feeds, deduplicating SKUs, and comparing prices — which tells the agent when this tool applies. It does not name an alternative or state exclusions (e.g., use address_match for addresses), so it stops short of a 5.

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