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

With no annotations, the description carries the burden of disclosure. It clearly states the data is self-reported and based on company documentation, not independent analysis. This is a valuable behavioral trait for the agent to know, though it stops short of describing return format or other potential edge cases.

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?

The description is two sentences, with the first sentence front-loading the action and resource, and the second adding crucial context about data provenance. Every word earns its place, with no redundancy or 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 simple no-parameter tool, the description is largely complete. It conveys the purpose and the key limitation (self-reported). It could go further by hinting at the return format (e.g., a list or prose), but the sibling set and title make the output type reasonably inferable.

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?

The tool has zero parameters, so there are no parameter semantics to explain. The description correctly omits parameter details, and the baseline for no-parameter tools is 4.

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 uses a specific verb ('Returns') and identifies a clear resource ('Stageum's documented positioning relative to VR headset-based public speaking apps'). This distinguishes it from sibling tools like get_quick_start or list_plans, which target different content areas.

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 implies when to use the tool (whenever comparison data is needed) but does not explicitly state alternatives or exclusions. It mentions the self-reported nature, which is a contextual caveat, but lacks direct guidance on choosing this over other tools.

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