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

With no annotations, the description carries the behavioral burden. It conveys a read-only listing/filtering operation and previews key output fields, but it does not explicitly state that no mutation occurs, how results are ordered, or how filtering behaves for partial matches.

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 efficient sentence that front-loads the action and target resource, then appends the key result details. Every clause earns its place.

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 list/filter tool with fully documented optional parameters, the description covers the main purpose and output highlights. It could be more complete by noting pagination defaults and the exact result shape, but these are largely covered by the schema and implied by the listed output fields.

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 each parameter already has a clear description with defaults and examples, so the description adds no new parameter semantics. The baseline of 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?

The description states a specific verb (list and filter), a clear resource (disc golf manufacturers/brands), and the scope of returned data (mold counts, speed ranges, category breakdowns, and top molds). This clearly differentiates it from the disc-focused sibling tools, since list_brands is the only brand-level tool.

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 description clearly implies this is the tool for brand-level overviews and filtering, while all siblings are disc-focused. It does not explicitly provide when-not-to-use guidance or route to alternatives, but the context is clear enough for an agent to select it correctly.

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