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

Goes beyond annotations by detailing the default model (Workers AI, free), optional Anthropic probing with BYO key, and the scoring range (0-100). No contradiction with 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?

Single paragraph, front-loaded with action and key details, every sentence earns its place. Efficient and clear.

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, but description adequately explains return structure (per-model {score, confidence, signals, raw_response} + combined view). Covers all necessary information for a tool of this complexity.

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 has 100% coverage, but description adds practical details: 'Omit for just workers-ai' for models, 'only needed if anthropic is in models' for _apiKey, and context helps disambiguate.

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') and resource ('LLMs for knowledge about an entity'), clearly distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on visibility scoring across models.

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 states use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and provides context about default vs paid models, but does not explicitly list when NOT to use or alternatives.

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