Browse Statistical Topics

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

Annotations already cover the read-only, idempotent, and non-destructive profile. The description adds useful behavioral context by promising the complete set ('all 149') and giving representative codes and labels. It doesn't mention ordering or language-dependent output, but that is less critical given the output schema.

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 core message is front-loaded in the first sentence, the examples earn their place by making return values concrete, and the last sentence is actionable. There is no filler or repetition.

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 simple, parameterless, read-only list operation with an output schema and complete parameter docs, the description covers purpose, when to use it, and what to do next. Nothing required to invoke it correctly 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 only parameter, language, is fully documented in the input schema with enum values, default, and meaning. The description adds no parameter-level guidance, so it stays at the baseline expected when schema description coverage is high.

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?

Opens with a specific verb and resource: 'List all 149 subject areas (topics) in StatFin database.' The topic examples make the resource concrete, and the final sentence distinguishes it from list_tables, so an agent can clearly tell what this tool produces.

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?

Explicitly states when to use it: 'when you don't have a specific search term,' and gives a clear next step to list_tables. It doesn't name search_statistics as the counterpart for specific queries, but the contextual cue is strong enough to guide selection.

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