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

No annotations are provided, so the description carries the full burden. It does not state whether this is a read-only lookup, whether matching is exact/fuzzy/partial, or how many results come back — significant gaps for a search-style tool.

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 front-loaded sentence that defines the operation, the input forms, and an illustrative example with zero 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?

For a one-param lookup with no output schema or annotations, the core intent is conveyed, but match semantics, result format, and read-only nature are left unstated.

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 0% and the single 'query' param is undocumented in the schema, but the description compensates by stating the query accepts either a part model number or a name, reinforced with a concrete example (BR9ES).

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 (reverse-lookup) and resource (applicable two-stroke vehicle models) keyed on a part number/name. It is clearly distinguishable from siblings like search_models and get_model_specs, though it does not name them 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?

The example ('spark plug BR9ES') implies when the tool applies, but there is no explicit when-to-use, when-not-to-use, or routing to alternatives such as search_models for forward lookups.

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