get_dataset_info

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

With no annotations, the description carries the burden of behavioral disclosure. It states the return type and lists some metadata fields, implying a read-only operation. Still, it does not clarify behavior when the dataset parameter is empty (it has a default), nor does it mention possible errors, permissions, or response structure beyond 'dict[str, Any]'.

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 well-structured with a front-loaded main statement, a use-case sentence, and an Args/Returns breakdown. However, the Returns section repeats the output type already visible in the output schema and 'and more' is vague, adding mild 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 two-parameter metadata tool, the description covers purpose, usage, and parameters. But it omits that both parameters are optional in schema (required: 0), leaving the impression that catalog and dataset are always needed. It also does not mention error conditions or how to obtain valid dataset identifiers, which is relevant given the sibling get_catalogs.

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 0%, so the description must compensate. It does provide a line for each parameter: catalog as 'Catalog identifier' and dataset as 'Dataset identifier' with an example ('85523NED'). This adds value beyond the bare schema property names, though it remains minimal.

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 uses a specific verb and resource: 'Fetch metadata for a single specific dataset.' It clearly scopes the tool to one dataset and indicates the return includes title, description, status, and modification date. This differentiates it from broad query tools like query_datasets and the more general get_metadata.

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 phrase 'Use this to quickly check whether a dataset is relevant' gives clear context for when to apply the tool. However, it does not explicitly mention alternatives or exclusions, such as when to prefer get_metadata or query_datasets.

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