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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and no destructiveness. The description adds behavioral detail: the return format (per-model score, confidence, signals, raw_response), the fact that Workers AI is free, and that the API key is passed directly to Anthropic (privacy). This provides useful context 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?

The description is three sentences with no fluff. It front-loads the core purpose ('Probe one or more LLMs...score visibility') and efficiently covers details like defaults and return format. Every sentence serves a purpose.

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?

Despite lacking an output schema, the description explains the return structure (per-model and combined view). Given 4 parameters (all described), no nested objects, and reasonable complexity, the description covers input, output, and use cases sufficiently. It could be slightly richer on edge cases (e.g., what if model is unsupported), but overall it is complete.

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 description coverage is 100%, so the schema already documents all four parameters clearly. The description adds little beyond the schema—it mentions the default model and the API key purpose, but these are already in the schema descriptions. Baseline 3 is appropriate as the description does not significantly augment parameter understanding.

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', 'score') and resource ('LLMs', 'visibility'), clearly stating it scores visibility (0-100) per model for a given entity. It also contextualizes the use case ('AI-marketing audits', 'pre-launch brand checks', 'competitive monitoring'), effectively distinguishing it from sibling tools like scan_competitor_ai_presence or compare_entities.

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 mentions default model (Workers AI) and optional Anthropic with BYO key, giving practical usage context. It identifies relevant applications (audits, brand checks, monitoring) but does not explicitly state when not to use the tool or compare with siblings, leaving some gap in exclusion guidance.

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