get_interest_suggestions

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

With no annotations, the description carries the behavioral transparency burden. It discloses useful traits: access_token is optional and a cached token is used if not provided, and the return is a JSON string with specific fields. This goes beyond the basic 'get' semantics.

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 with a clear opening sentence, followed by a concise Args section and a Returns line. Every line earns its place, with no redundant or verbose content.

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?

The description covers all core aspects: purpose, parameters, token handling, and return format. It does not mention error cases or network calls, but for a straightforward suggestion tool with an output schema, it is sufficiently complete for correct invocation.

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 description coverage is 0%, so the description must fully explain parameters. It does so effectively: interest_list with an example, access_token with caching note, and limit with default value. This adds significant meaning beyond the schema's raw type definitions.

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 tool's purpose: 'Get interest suggestions based on existing interests.' This specifies a verb ('get') and resource ('interest suggestions') and distinguishes it from sibling tools like search_interests by emphasizing the suggestion generation from existing interests.

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 usage is implied from the description: provide an interest_list to receive related suggestions. However, there is no explicit guidance on when to use this tool versus alternatives like search_interests or estimate_audience_size, and no exclusions are mentioned.

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