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

Annotations already mark this as read-only, idempotent, and non-destructive; the description goes further by disclosing that it is a demo of a paid product, computed deterministically from the index, and in production uses consented/compensated cohort signals without profiles leaving the user's agent. It also discloses a clear fallback for catalog-only brands, adding substantial behavioral context beyond the 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?

The description is dense and front-loaded with the core purpose, and every clause adds meaningful behavioral or scoping detail. It loses one point for being a single long paragraph with a slightly idiosyncratic aside ('publishing more truth, not paying Graviti') rather than clearly separated purpose, behavior, and fallback sections.

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?

For a single-parameter, read-only report with no output schema, the description is nearly complete: it enumerates the report contents, notes the deterministic computation, explains production data sources and privacy constraints, and covers the insufficient-evidence fallback. It only stops short of describing the exact response structure or field names an agent would need for programmatic parsing.

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 single parameter `brand` is already fully documented in the schema with type and an example, and schema coverage is 100%. The description adds no brand-specific parameter nuance, but none is needed given the schema's completeness.

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 deliverable: a brand gap report showing attribute gaps vs. the category leader, open flags, cohort blind spots, disclosure dimensions, and an info_quality score with publishable fixes. It distinguishes this from sibling tools by focusing on brand-vs-leader gap analysis and evidence sufficiency, not category landscape, ledger, or conversion reporting.

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 implies this tool is for assessing a brand's competitive gap and mentions the catalog-only fallback, but it does not explicitly state when to use this tool instead of siblings like category_landscape or match_intent. Some situational guidance is present, but no exclusions or alternative-selection rules are given.

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