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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable context: no auth required, the limitation of no full-text search API, and that it returns only dataset_id and title. This goes beyond the annotations without contradicting them.

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 concise and well-structured. It front-loads the purpose, then details the search behavior, then explains the return value and downstream use, all in three sentences with no 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?

For a simple search tool with optional parameters and an output schema, the description covers everything an agent needs: what it does, how to filter, the limitation, the return type, and the intended follow-up tools. It is complete for effective 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 100%, so all parameters are documented. The description reiterates the search behavior (case-insensitive substring on title/dataset_id) but adds no new semantics beyond the schema. Baseline 3 is appropriate since the schema does the heavy lifting.

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 function: browsing/searching the Statistik Austria open-data catalog. It specifies the scope (~540 datasets), the filter mechanism, and the return value (dataset_id + title). It distinguishes itself from siblings by naming the specific Austrian data source and the intended downstream tools (dataset_metadata, dataset_data). No ambiguity.

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 usage for discovering Austrian datasets and mentions the returned IDs are for use in other statistik-austria tools, but it does not explicitly state when to choose this over alternatives like category_codes or dataset_metadata. It provides a clear hint about the pipeline but lacks explicit exclusion or alternative guidance.

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