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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description reinforces this explicitly: 'Every call is read-only and idempotent, so after correcting the input it is always safe to retry.' It also discloses error handling behavior ('never raises a protocol error — it returns {"error": ...}') and notes it is free. No contradictions and meaningful additional context.

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 well-structured with a short intro, a detailed example block, and usage guidance. It is slightly long but every sentence adds value. The information is front-loaded with the core purpose and key constraints. Minor redundancy in repeating the 'real writing' constraint could be trimmed, but overall it is efficient for the necessary detail.

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 the simple 2-parameter schema, presence of annotations and output schema, the description fully covers all aspects: workflow (input → profile), error behavior, integration with sibling tools, requirements for samples, and idempotency. No gaps remain for an agent to understand how to select and invoke this tool 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% with both parameters clearly described. The description adds significant context beyond the schema: example input format ('{"samples": ["<email text>", "<blog post>"]}'), explanation of default label behavior, and details about output fields that help understand how parameters shape the result. This elevates meaning beyond the baseline 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 'Build a measurable voice profile from samples of a person's real writing.' It identifies the specific verb 'build' and resource 'voice profile' and distinguishes from sibling tools by stating 'Not for scoring an unknown draft (ai_tell_scan)' and references humanize_plan/verify_rewrite. The purpose is explicit and unambiguous.

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 provides explicit when-to-use guidance ('Feed it 2+ samples... use the result with humanize_plan / verify_rewrite') and when-not-to-use ('Not for scoring an unknown draft (ai_tell_scan) and not on text the person did not write'). It also warns about errors and gives retry advice, making it clear when to apply this tool versus alternatives.

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