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

Annotations already declare readOnly/openWorld/idempotent, and the description adds meaningful operational details: the default free model, the requirement to pass `_apiKey` to probe Anthropic, and the fact that the user pays Anthropic directly for those calls. This goes beyond the annotations by disclosing an external dependency and cost implications.

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 core function, followed by pricing/behavioral details and use cases. No filler; every clause adds information.

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?

Covers the tool's purpose, parameters (via schema), behavioral details (default model, BYOK), return structure (per-model score/confidence/signals/raw_response + combined view), and ideal use cases. No output schema exists, so the description correctly fills that gap.

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 already provides 100% coverage with clear descriptions for all parameters (entity, models, _apiKey, context). The description adds no new parameter details beyond the schema, so baseline 3 is 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?

States a specific action ('Probe one or more LLMs') and a clear outcome (score visibility 0-100 per model). The description also specifies return structure and use cases, distinguishing it from siblings like ask_pipeworx or scan_competitor_ai_presence by focusing on multi-model visibility scoring.

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?

Explicitly lists use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') which gives clear context for when to use. However, it does not explicitly state when not to use it or name alternatives, so it falls short of the top score.

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