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

Annotations already declare safety traits (readOnlyHint, idempotentHint, etc.). The description adds value by detailing cost implications (free default, BYO for Anthropic) and the return structure (score, confidence, signals, raw_response). No contradictions.

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 a single, well-structured paragraph. It front-loads the main action and then provides supporting details. Every sentence adds value; no wasted words.

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?

For a tool with 4 parameters and no output schema, the description covers purpose, usage context, parameter roles, and return value structure comprehensively. It leaves no obvious gaps.

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 baseline is 3. The description adds meaning beyond schema: it conveys that 'workers-ai' is the free default, that _apiKey is only needed for Anthropic, and that 'context' helps disambiguate. This provides richer guidance for parameter usage.

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 precisely states the tool's function: probing LLMs for entity knowledge and scoring visibility (0-100) per model. It names the default model and the optional Anthropic integration, clearly distinguishing it from sibling tools like 'scan_competitor_ai_presence' or 'deep_research'.

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 says when to use the tool: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' Explains the default model and when to provide _apiKey. Does not explicitly mention when not to use it, but the context is clear.

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