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

Beyond the readOnly/idempotent annotations, it discloses the data-coverage caveat (only products 'Greener Closet has analyzed'), the return contents (scores, synthetic-fiber counts, sourced claims), and two hard scope limits: it never rates the company itself and cannot confirm PFAS finishes. These are exactly the interpretive constraints an agent needs before summarizing results.

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?

Purpose leads, examples follow, limitations and sibling routing close it out — a deliberate front-loaded ordering. Every sentence carries unique information: the examples disambiguate intent, the negative constraints prevent misuse, the final sentence routes elsewhere.

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 single-parameter, read-only lookup with an output schema already documenting return shape and annotations covering safety, the description fills the remaining gaps: what data coverage exists, what it will not assert, and which sibling handles adjacent intents. Nothing material 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% and the schema already supplies brand-name examples, so the description mainly reinforces that the input is a free-text brand name via its example questions. It adds no normalization guidance (casing, aliases, abbreviations) beyond what the schema states, so the baseline 3 applies.

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 ('check' a 'clothing brand') and immediately scopes it with concrete example questions. It also explicitly contrasts itself with check_product and find_alternatives, so the agent can tell the three apart without opening any schema.

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 triggers (materials or sustainability questions about a brand) with five sample phrasings, plus explicit when-not-to-use routing: single product to check_product, brand discovery to find_alternatives. It even names a question type it cannot answer (PFAS finishes), preventing an over-eager call.

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