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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely useful behavior: the drop is computed against the shop's own 30-day low and never against a shop's advertised "was" price, and results carry site, product page and shop link. It stops short of describing result ordering guarantees or volume beyond the schema's limit.

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?

One dense sentence with zero filler, front-loaded on the headline result before the metric definition and included fields. Every clause earns its place, including the parenthetical ruling out advertised "was" prices.

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 three-parameter, no-output-schema read tool this is close to complete: it defines the ranking basis, the market scoping, and the shape of each result (site, product page, shop link). Only the ordering/pagination behavior and default locale handling in the answer are left implicit, and the schema covers the defaults.

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 the baseline is 3; the description modestly adds meaning by framing locale as a market that determines shops, prices and currency, and by scoping results to "the locale's market". Sites and limit semantics are left entirely to the schema, which already handles them well.

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?

The description names a specific resarah of result (largest real price drops right now, per locale market, across sites) and defines the drop metric precisely as a shop's current price against its own lowest price of the preceding 30 days. That methodology distinguishes it from compare_prices or search_products, though the siblings are never named explicitly.

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 — this is a browse/discovery tool for aggregate price drops, not a lookup for a specific product — but the description never states when to prefer it over compare_prices or search_products, nor any exclusions. The reader must infer the routing from the metric definition.

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