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

Beyond annotations, the description adds key behavioral details: the default model is free, probing Anthropic requires a BYO key with direct costs, and the return structure includes per-model fields plus a combined view. These complement the readOnlyHint and idempotentHint without contradiction.

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?

Three sentences, front-loaded with the primary function, efficient and information-dense. Every sentence earns its place: what it does, default behavior and key parameter nuance, plus return and use cases.

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?

The description is complete for a tool with rich annotations and full schema coverage: it explains the purpose, default behavior, BYO key cost nuance, return shape (since no output schema exists), and relevant use cases. Minor gap: doesn't mention any limitations or model count variability, but adequate.

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%, so the baseline is 3. The description adds extra meaning by naming the default model (Workers AI Llama-3.3-70b) and clarifying the cost implication of _apiKey, which goes slightly beyond the schema's technical notes.

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 clearly states a specific verb (probe), resource (LLMs), and outcome (visibility score per model 0-100). It distinguishes itself from sibling tools like ask_pipeworx by focusing on what models know and scoring it, rather than just querying.

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 provides explicit use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. However, it does not mention when not to use it or name specific alternatives, so it stops short of a 5.

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