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

Even with annotations declaring readOnly/idempotent/non-destructive, the description adds valuable behavioral context: the default free model, the BYO-key requirement for Anthropic with direct payment to Anthropic, and the per-model output structure. This goes beyond what annotations provide and sets clear expectations for cost and external calls.

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 (three sentences) and front-loaded with purpose, then defaults, then return format, then use cases. Every sentence adds distinct information with no filler. It earns a top score for efficiency.

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?

With no output schema, the description compensates by detailing the per-model response fields (score, confidence, signals, raw_response) and the combined view. It covers inputs, defaults, pricing, and output in a concise package, making it complete for a moderately complex tool.

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% and each parameter already has a thorough description. The tool description adds minimal parameter-specific meaning beyond the schema—it mentions the default model and cost implication for _apiKey, but these are more behavioral than semantic. Baseline 3 is appropriate when the schema does the heavy lifting.

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 ('Probe') and names the exact resource (LLMs) and subject (business/brand/product/topic). It clearly states the scoring output (visibility 0-100 per model), which distinguishes it from vague 'check' tools. The return format and default model details further pin down the tool's identity.

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 gives clear use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') but does not explicitly mention when not to use it or name alternatives (e.g., scan_competitor_ai_presence). It provides context for when to invoke but lacks explicit exclusions.

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