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

Beyond the annotations (readOnly/openWorld=false/idempotent), it discloses result ordering (highest score first), what the results contain (disclosed fiber breakdown), and the fail-closed semantics of exclusions (only products with full composition disclosed are excluded). It also notes it does not filter by price, which sets a clear expectation. Missing only a note on pagination/breadth or how scores are derived.

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 primary use case and sibling routing, with no filler sentences. The five examples are slightly repetitive in aggregate but each maps to a distinct parameter combination, so they earn most of their space.

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 an 8-parameter read tool with an output schema, the description covers scope, ordering, exclusion semantics, PFAS caveat, price limitation, and sibling routing. Return-value detail is correctly omitted since the output schema carries it.

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 75% and the description adds real semantic value on top of it, especially that fiber exclusions apply only to products with full composition disclosure and that recycled variants are excluded too. It implicitly rules out a price parameter. The two undocumented parameters (limit, department) are self-evident from name and enum, so the gap is minor.

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+resource (find clothing by fiber content) and immediately differentiates from siblings: 'Do not use this to check one product or brand (use check_product or check_brand)'. The concrete query examples ("leggings like Lululemon without polyester", "polyester-free socks") make the retrieval intent unambiguous.

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 explicit when-to-use framing ("Use this when the user wants clothing with specific fiber content or an alternative to a brand") and explicit when-not-to-use with named alternatives (check_product, check_brand). It also scopes an adjacent-but-different need by warning that fiber content is not a PFAS test, preventing misfires.

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