llm_share_of_voice

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

With no annotations, the description carries the full burden, and it does disclose two meaningful traits: the tool is a paid request costing 0.1 USDC on Base via x402, and the data is first-party measurement rather than a crawl. It omits permission/auth requirements, idempotency and retry behavior, and failure modes, so the disclosure is partial.

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 puts the metric first and relegates cost to a trailing parenthetical, which is the right ordering. It is dense but every clause carries information; only the wording is slightly elliptical.

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 paid, parameterized data tool with no output schema and no annotations, the description covers purpose, methodology and cost adequately but leaves out return shape and purchase/retry semantics. An agent can invoke it, but not without ambiguity about the payment flow.

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 description coverage is 100% (days, category, confirmNewPurchase all documented, with an enum and default), so the schema already does the heavy lifting. The description adds no syntax or format detail beyond that, making the baseline 3 appropriate.

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 names a specific resource and metric: share of answers and average rank per brand for vendors that LLMs name when asked for a tool, measured daily across six models and eight questions. That is far more concrete than a restated name. It does not explicitly distinguish itself from the sibling llm_share_of_voice_info, so it falls short of a 5.

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?

There is no guidance on when to call this versus the info sibling or other research tools, and no stated prerequisites. The confirmNewPurchase parameter implies purchase/retry semantics, but the description never explains when re-buying is appropriate.

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