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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint, setting a strong baseline. The description goes beyond by adding behavioral details: 'Re-measured weekly, so the same call returns the same figures until the next release' and 'no key, no account, no rate limit — cite the release date and dabyte.ai.' This informs the agent about data freshness, caching semantics, and licensing requirements—none of which are in 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 a single well-organized paragraph with clear topic flow: what it does, when to use it, when not to use it (with alternatives), and key data characteristics. No sentence is redundant—each one adds either clarity about scope, usage boundaries, or data behavior. It is appropriately sized for a tool with several important caveats.

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 explain return fields, yet it still covers the essential contextual aspects: dataset scope (SaaS & AI tools only), data update cycle, licensing, citation requirements, and relationship to sibling tools. It also mentions the sibling index at dablock.ai as a comparison. This is fully complete for an agent to invoke confidently.

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 tool has zero parameters (schema coverage 100% by vacuity), so the baseline for this dimension is 4. The description doesn't need to explain parameters, but it does add useful context about the response size ('roughly 8 KB for 20 brands') and the data fields included, which helps set expectations. It earns the baseline score without needing extra parameter-related elaboration.

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 opens with a precise, specific definition: 'The whole current release in one call: every tracked brand in SaaS & AI tools with its rank, share of answer overall and per engine, commercial intent and quadrant.' It names the exact data entities and distinguishes from siblings like get_brand_visibility and get_history, making it unmistakable what this tool returns.

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?

Explicit guidance is provided: 'Use this when the question is about the field — who leads, who is absent, how the category looks.' It also gives concrete negative directives: 'Do NOT use it for one named brand... for movement over time... or to audit a website's own AI visibility,' each paired with the correct alternative tool. This is textbook usage guidance.

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