Get Inferred Interests by Email

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

Annotations already provide readOnlyHint=true, and the description adds valuable context: the data is 'experimental machine learning-generated' and 'based on public profile information.' This discloses the non-deterministic/experimental nature of results and their source, going beyond what annotations offer. There is no contradiction with 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 main purpose is front-loaded in the first sentence. The hint and examples are purposeful additions that aid tool selection and usage, though the hint could be seen as slightly tangential. Overall it remains compact and substantive without redundancy.

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 a single parameter, full schema coverage, an output schema present, and annotations indicating a read-only operation, the description is complete enough. It explains what the tool returns and adds the experimental caveat and a usage preference. Nothing critical for correct invocation is missing.

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 100% of the parameter description, including the normalization and hashing behavior. The description's examples ('user@example.com') add illustration but no new semantic meaning beyond the schema. The baseline of 3 applies because the schema already does the heavy lifting.

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 clear action ('Retrieve AI-inferred interests') with a specific resource and access method ('for a Gravatar profile using an email address'). It distinguishes itself from siblings like get_inferred_interests_by_id (by ID vs email) and get_profile_by_email (profile vs interests) without requiring schema inspection.

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 hint explicitly says to prefer explicit profile interests over inferred ones, giving a clear condition for when NOT to use this tool and effectively routing to the profile tool as the alternative. It does not explicitly mention the by-ID sibling, but the email-vs-ID distinction is embedded in the tool name and examples provide concrete invocation patterns. This is clear context with one notable exclusion, but it lacks a full when-to-use-this-tool statement versus the ID variant.

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