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

Annotations already declare readOnly and open-world, so the safety profile is covered. The description adds real value beyond that by disclosing data provenance: most facets are read live from GOAT's search API while product_types and activities are maintained, which matters for freshness expectations.

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?

A single dense sentence that front-loads the resource and purpose, then the facet list and provenance. Every clause earns its place; slightly long but no filler.

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?

An output schema exists, so return values need not be explained. The description covers what the tool provides, which facets, and where they come from, which is sufficient for a zero-parameter facet-lookup 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?

With zero parameters, the baseline is 4. The description names the facet dimensions returned (categories, colors, genders, conditions, brands, years), which characterizes the output rather than params, but no parameter clarification is needed here.

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 resource (GOAT's accepted facet values) and explicitly ties it to the sibling tool goat_search's filter parameters, enumerating the facets covered. An agent can distinguish it from goat_search, goat_collection, and goat_suggest without opening schemas.

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 by naming goat_search's filter parameters, so the agent can infer this is the lookup to call before filtering. However, it never explicitly states when to call this versus goat_search itself, nor gives a when-not condition. Adequate but with a clear gap.

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