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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds meaningful behavioral context: the default model is Workers AI (free), Anthropic calls require a BYO key and incur direct costs, and it returns a structured per-model result. This goes beyond the annotation baseline, though it doesn't address rate limits or latency.

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 three well-structured sentences with the core purpose front-loaded. Details like the default model and cost implications are placed in parentheticals, and no sentences are wasted. It's dense yet easy to scan.

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?

There is no output schema, so the description appropriately explains the return shape: per-model {score, confidence, signals, raw_response} plus a combined view. It also covers model selection, costs, and use cases, making it fully contextual for a 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%, so the baseline is 3. The description adds value by clarifying the default model, explaining that _apiKey enables Anthropic probing and that you pay Anthropic directly, and noting that context helps disambiguate common names. This supplements the schema without being redundant.

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 opens with a clear verb ('Probe') and resource ('one or more LLMs'), specifies the output (visibility score 0-100 per model), and adds concrete use cases (AI-marketing audits, pre-launch brand checks). This distinguishes it from sibling tools like ask_pipeworx or scan_competitor_ai_presence by focusing on multi-model visibility scoring rather than Q&A.

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 use-case context ('Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains when to use the optional Anthropic model ('pass _apiKey to also probe Anthropic'). However, it doesn't explicitly name alternatives or state when not to use this tool, so it falls 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.