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

Beyond annotations, the description discloses important behaviors: re-measured weekly, same call returns same figures until next release; unknown slug error names every valid slug; absence from index vs measured zero; and data licensing/citation requirements. This goes well beyond readOnlyHint and 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?

The description is longer than average but every sentence adds distinct value: purpose, alternatives, error handling, data meaning, scope, refresh cadence, and licensing. It is front-loaded with the core purpose, and the structure makes it easy to parse, though slightly dense.

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?

With an output schema present, the description doesn't need to detail return fields, but it still mentions them. It covers error recovery, absence vs zero distinction, measurement frequency, alternative tools, and licensing requirements. No meaningful gaps remain for an agent to use 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?

The input schema already fully documents the slug parameter with pattern and example. The description adds practical value by clarifying 'Takes a slug, not a display name' and directing users to call list_tracked_brands or read from get_visibility_index if unsure, which helps correct 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 clearly states the tool's function: 'One brand's standing in the current DABYTE release' with specific fields like share of answer, rank, quadrant, and panel prompts. It explicitly differentiates from siblings by directing users to get_visibility_index for the whole field and get_history for trends over time.

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: 'Use this when a specific brand is named.' It also names alternatives and preconditions: 'call list_tracked_brands first if you are unsure' and 'For the field as a whole use get_visibility_index; for this brand over time, get_history.' This is model usage guidance.

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