Get Benchmark Metadata

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is fully structured and needs no restating. The description adds value by naming the categories of content exposed (dimensions, datasets, regions, taxonomy versions). It does not mention any access requirements, rate limits, or whether the catalog is static or updated per release, which is modestly relevant for an open-world discovery call.

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?

A single front-loaded sentence with no filler. The verb comes first and the enumerated content follows, so an agent scanning the first few words gets the meaning immediately.

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 zero-parameter discovery tool with a full output schema and complete annotations, the description covers the essentials. The one gap is that it never tells the agent that this metadata is consumed by the benchmark query siblings (get_occupation_benchmark, query_core_lmi), which would complete the workflow picture.

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 tool takes zero parameters, so the 4 baseline applies; there is no parameter meaning the description could add. The description does not waste words restating an empty 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 gives a specific verb ("Discover") and enumerates the resources returned: benchmark dimensions, datasets, regions, and taxonomy versions. It is clear what the tool does. It does not explicitly distinguish itself from metadata siblings like get_classification_metadata or get_core_lmi_metadata, so an agent must infer the benchmark-vs-other-domain split from names alone.

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?

"Discover" implies this is an exploratory entry point to be called before requesting benchmark data, which is implied usage. However, there is no explicit statement of when to use it versus the other metadata tools, no prerequisites, and no note on whether it should be called first to learn valid dimension/region values.

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