Get Inferred Interests by ID

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

Annotations already mark this as read-only and open-world, and the description adds context by calling the data 'experimental machine learning-generated' and based on 'public profile information.' This aligns with the annotations and gives the agent useful expectations about data provenance and variability without contradicting any hint.

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: a clear purpose sentence, a behavior sentence, a usage hint, and examples. It is front-loaded and easy to scan. There is minor redundancy between 'AI-inferred' and 'machine learning-generated interest data,' but it does not hurt comprehension.

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 lookup with a detailed input schema and an output schema, the description covers the essential behavior, data source, experimental nature, and example invocations. It is complete enough for an agent to invoke correctly, though it could strengthen tool selection by mentioning the by_email sibling.

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 covers the profileIdentifier parameter thoroughly, including normalization and hashing details, so the description does not need to repeat it. The examples add minor value by showing realistic invocation phrasings, but they go beyond the schema only slightly.

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 states a specific verb and resource: 'Retrieve AI-inferred interests for a Gravatar profile using a profile identifier.' It clearly identifies what the tool returns and the input basis. However, it does not explicitly differentiate itself from the sibling get_inferred_interests_by_email; differentiation relies mostly on the tool name and examples.

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?

There is no explicit guidance on when to use this tool versus get_inferred_interests_by_email or the profile lookup tools. The hint about preferring explicit Gravatar profile interests over inferred interests is useful, but it does not address endpoint selection. The examples illustrate phrasings rather than provide decision criteria.

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