get_recommended_topics

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

There are no annotations, so the description carries the full burden of behavioral disclosure. It only restates the action implied by the tool name and does not mention the nature of the recommendations, return format, potential side effects, or any prerequisites beyond the text reference.

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 sentence with no redundant wording. It is concise and front-loaded, clearly stating the operation and the required input.

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 tool is simple with one parameter, but there is no output schema and no annotation to clarify behavior. The description does not explain what 'recommended topics' means or how this tool differs from several sibling tools that also relate topics and text references, leaving some selection ambiguity.

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 schema already describes tref as 'The text reference.' The description's phrase 'specific text reference' adds no meaningful semantic detail beyond the schema, so the baseline score of 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?

The description uses a specific verb and resource ('get recommended topics') and identifies the input ('a specific text reference'). The word 'recommended' helps distinguish it from broader siblings like get_topics or get_all_topics, though it doesn't explain what 'recommended' means.

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 description implies when to use the tool: when you have a text reference and need recommended topics. However, it provides no explicit guidance about when not to use it or how it differs from related siblings such as get_related, get_topics, or get_topic_graph.

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