Modern AI Brand Recommendation Rate

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

Annotations already establish readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds valuable content context (refresh cadence, methodology links, AI surface measured), but does not discuss operational details like auth, rate limits, or return structure beyond the listed topics.

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?

A single, front-loaded sentence lists the contents without filler. Every element earns its place and there is no redundant preamble.

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 parameterless, read-only informational tool with no output schema, the description covers what the agent will get. The main gap is sibling routing: it does not tell the agent when to prefer this over lookup_brand_recommendation_rate.

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 and the schema is empty, so the baseline of 4 applies. There is no parameter-level meaning to add or omit.

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 names the exact artifacts returned—definitions of Recommendation Rate and Recommendation Inclusion Rate, AI surface, refresh cadence, and methodology links—so the resource and scope are clear. It does not distinguish itself from the sibling lookup_brand_recommendation_rate, which likely returns actual rate values, so it falls short of a 5.

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?

No when-to-use guidance or explicit alternative is provided. An agent can infer this is for methodology rather than raw values, but the description never states that condition or routes to/from the sibling tool.

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

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

Annotations cover read-only and open-world, and the description adds valuable behavior: rate-limited per anti-scrape policy, free, single-brand only, and returns a public record URL and measured date for citation. It doesn't detail error behavior or rate-limit specifics.

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?

One front-loaded sentence that defines the metric, return values, and constraints without redundancy. Every clause earns its place, though the sentence is 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 no output schema, the description helpfully states the return values (record URL and measured date) and defines the metrics. It omits not-found/error behavior and does not reference the methodology sibling, but is otherwise complete for a one-parameter lookup.

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 the single parameter is documented in the schema. The description adds no syntax or format details beyond 'single-brand lookup only', so baseline 3 applies.

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?

States a specific verb 'Look up' and resource 'a brand's AI Recommendation Rate', and defines the two metrics (top pick vs inclusion). It does not explicitly differentiate from sibling get_recommendation_rate_methodology, though the resource itself distinguishes lookup from methodology.

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?

Mentions 'Free, single-brand lookup only' and rate-limit policy, giving usage constraints. However, it never says when to use this tool versus the methodology sibling or when not to use it.

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