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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive, so the safety profile is covered. The description adds valuable context: it mentions the default free model, that Anthropic probing requires a BYO key with direct payment, and the exact return shape (per-model {score, confidence, signals, raw_response} + combined view). This goes beyond annotations.

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?

Two sentences, front-loaded with the core action and output, then use cases. Every phrase earns its place—no fluff or repetition. Highly efficient.

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?

No output schema exists, but the description explicitly describes the return format. It covers cost implications, default behavior, and typical use cases. This is sufficient for an agent to correctly select and invoke the 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 parameters are well-documented. The description adds semantic nuance by explaining the default model selection (_apiKey only needed for Anthropic) and that context helps disambiguate common names. This enriches the schema descriptions.

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 specific verb+resource: 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' This clearly states the core function and distinguishes it from sibling tools like ask_pipeworx or scan_competitor_ai_presence by focusing on cross-LLM knowledge probing and 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?

Provides explicit use cases: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also clarifies when to use the optional Anthropic model (when _apiKey is provided). It does not explicitly contrast with alternatives, but the application scenarios are clear enough to guide selection.

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