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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: that probing Anthropic requires a user-provided API key and that users pay Anthropic directly. It also details the return structure (per-model score, confidence, signals, raw_response) and a combined view, going well beyond the 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 four sentences, each serving a distinct purpose: stating the core function and scoring, explaining default and optional API key, detailing return format, and listing applications. No superfluous information, well front-loaded.

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 4 parameters fully described in schema and additional context in description, the tool is fully specified. The description includes return structure (per-model fields and combined view), which compensates for the lack of output schema. The use cases provided give adequate context for an agent to decide when to invoke this 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 the description enriches each parameter with practical context: entity is 'the thing to ask about', models defaults to workers-ai, _apiKey is needed only if Anthropic is used, and context helps disambiguate. This adds significant meaning beyond 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 clearly states the tool's purpose: probing LLMs for knowledge about an entity and scoring visibility (0-100) per model. It uses specific verbs (probe, score) and identifies the resource (LLMs), making the intent unambiguous. The tool's unique function is evident among 40+ siblings.

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 explicitly outlines default model usage (Workers AI Llama-3.3-70b) and the optional activation of Anthropic with an API key. It also suggests use cases like AI-marketing audits and brand checks. However, it does not explicitly state when to avoid this tool or mention alternatives (e.g., scan_competitor_ai_presence), which would improve guidance.

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