explore_catalog

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 mentions 'cheap on tokens' which is helpful, but does not explicitly state that the operation is read-only or idempotent. The description covers the behavioral scope adequately.

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 plus a usage hint, with no redundant information. Every sentence adds value, and the structure is front-loaded with purpose.

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 no output schema, the description explains the return type (aggregates, not datasets). It covers purpose, usage, and parameters. Could detail the response format (e.g., fields returned) but is sufficient for selection.

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 80% with descriptions for 4 of 5 parameters. The description adds context (e.g., 'query' narrows topic, group_by options), but does not significantly enhance understanding beyond the schema. Baseline 3 is appropriate.

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 it provides aggregate catalog overview (count, publishers, categories/formats) and explicitly distinguishes itself from siblings by noting it does not return datasets themselves. The Ukrainian text gives concrete usage examples.

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 gives explicit use cases (e.g., 'who publishes most data about X') and implies when not to use (when you need actual datasets). It does not explicitly name sibling alternatives but 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.