llm_share_of_voice_info

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

With no annotations, the description carries the full burden, but it does disclose the return content (categories, models, questions, amount of available data) and flags that access is free. It omits any indication of whether the underlying data is live, cached, or gated, and says nothing about what an empty result means.

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?

One short sentence that front-loads the resource name, which is good. But '(free)' is duplicated verbatim at the end, which is plainly a structural artifact and wastes the only remaining token budget.

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 zero-parameter informational tool with no output schema and no annotations, the description is nearly sufficient: it enumerates what information is returned, which is the main thing an agent needs. Slightly more on when to reach for it would make it fully self-contained.

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 takes zero parameters, so there is no parameter semantics to explain; the baseline of 4 applies. The schema trivially covers 100% of the (empty) surface.

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 communicates the resource ('LLM-Share-of-Voice measurement') and its scope (categories, models, questions, data volume), which lets an agent separate it from the sibling llm_share_of_voice that performs the measurement itself. However it has no verb and never says outright that this is an informational/metadata lookup, so the action is only implied.

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?

Nothing states when this should be called versus the sibling llm_share_of_voice. The only usage cue is the parenthetical '(free)', which hints at cost but not at call conditions or prerequisites. An agent must infer that this is a pre-flight discovery call.

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