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

Annotations already declare readOnly, openWorld, and idempotent hints, so the description builds on them by revealing important behavioral details: default model, external payment implications, API key forwarding, and return structure. This exceeds mere annotation repetition but lacks deeper details like rate limits or failure modes.

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 compact and front-loaded: the first sentence states the core purpose, the second covers model selection and costs, and the third summarizes output and use cases. Every sentence contributes without redundancy.

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?

Given there is no output schema, the description compensates by naming the per-model response fields (score, confidence, signals, raw_response) and the combined view. It also provides practical context (default free model, BYO-key cost). However, it does not mention any limits on number of models or potential error conditions, leaving a minor gap for a tool with moderate complexity.

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 input schema covers 100% of parameter descriptions, so the description does not need to add much. It does reinforce the meaning of '_apiKey' and the default model, but the schema already provides the necessary semantics. Thus the baseline score 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 clearly states the tool's function with a specific verb ('probe'), resource (LLMs), and scope (score visibility 0-100 per model). It also includes distinct use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) that differentiate it from sibling tools.

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?

It provides clear contexts for use (audits, brand checks, monitoring) and explains model options (default free workers-ai vs BYO-key Anthropic). However, it does not explicitly state when not to use it or name alternative tools, so it falls short of the highest bar.

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