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

Beyond annotations (safe, idempotent), description adds that it makes external LLM calls, explains cost implications for Anthropic, and describes return structure (per-model score, confidence, signals, raw_response).

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?

Three sentences with no waste: first sentence defines purpose and output, second sentence covers configuration, third sentence lists use cases. Front-loaded and efficient.

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?

Given no output schema, description sufficiently details return format and covers all parameters. Context about use cases and required fields is complete for an agent to invoke correctly.

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 description adds value by clarifying default model, API key purpose, and providing concrete examples (e.g., 'Pipeworx', 'Acme Corp pricing') that aid agent 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 clearly states the tool probes LLMs for visibility scores (0-100) per model, distinguishing it from siblings like ask_pipeworx or scan_competitor_ai_presence by focusing on multi-model 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 explicit use cases (AI-marketing audits, pre-launch brand checks) and explains when to use default vs Anthropic API key, but does not mention alternatives or when not to use.

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