list_topics

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

No annotations are provided, so the description carries the full burden. It describes the read-only navigation behavior, including how to drill down and what information each item provides (has_subtopics, has_content). No destructive or mutating behavior is implied, and it is transparent about the hierarchy traversal.

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 two sentences with efficient wording. It front-loads the core purpose and follows with actionable examples. Every sentence adds value 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?

Given the parameter count (1) and the existence of an output schema, the description is complete. It covers top-level browsing, drill-down, and navigation cues (has_subtopics, has_content) with a pointer to a related tool.

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 schema has one parameter (parent_id) with 0% description coverage. The description adds meaning by explaining that passing a numeric topic id (e.g., '1245' for Economy) lists its subtopics, and that null gets top-level topics. This is valuable beyond the raw schema.

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 browses the ONS topic hierarchy, a taxonomy over the whole catalogue. It distinguishes from sibling tools (like list_datasets) by focusing on topics and provides examples of top-level topics (Economy, Employment, etc.) and drill-down behavior.

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?

It explains when to call with no argument for top-level topics and when to pass parent_id to list subtopics. It also mentions the has_subtopics and has_content flags and hints at using get_topic_content. While it doesn't explicitly exclude other uses, the context is clear.

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