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

Discloses default model, cost implications for Anthropic ('BYO key — you pay Anthropic directly'), and return structure ('per-model {score, confidence, signals, raw_response} + combined view'). Consistent with annotations (readOnlyHint, openWorldHint, idempotentHint).

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?

Five well-structured sentences, front-loaded with core purpose, no wasted words, includes both behavioral and practical details.

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?

Covers all aspects: purpose, parameters, return format, usage scenarios, pricing side-effect. No output schema but return fields listed explicitly. Complete for a 4-parameter 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% with descriptions. Description adds value by clarifying default behavior for 'models' parameter ('Default model is Workers AI Llama-3.3-70b (free)').

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?

States explicit purpose: 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' Clearly distinguishes from siblings like entity_profile, deep_research by focusing on AI visibility 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 clear use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' Lacks explicit when-not or direct sibling alternatives, but context is strong enough for an agent to infer appropriate usage.

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