describe_dataset

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

Annotations already provide readOnlyHint=true, and the description aligns by describing a non-mutating inspection operation. It adds useful behavioral context by specifying the returned information and framing the tool as a preliminary inspection step. No contradiction.

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?

Two sentences with no filler. The primary output is stated first, and the second sentence provides a practical usage guideline. Every sentence earns its place.

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 small read-only tool with an output schema, the description covers what it returns and when to call it. The main gap is parameter semantics, but the simple required parameters and the output schema keep it sufficiently complete for 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 documentation coverage is 0%, and the description does not explain what 'libref' and 'table' mean beyond the phrase 'SAS data set.' An agent must rely on parameter names and SAS domain knowledge to understand that libref is the library reference and table is the dataset name.

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 states a specific action ('Return the columns of a SAS data set') and a specific resource, enumerating the metadata fields plus row count. This distinguishes it from sibling tools like list_datasets or sample_rows by describing a metadata introspection operation.

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?

It gives an explicit usage recommendation: 'Call this before writing code against a table you have not already inspected.' It does not name alternative tools or explicitly state when not to use it, so it stops short of full routing guidance.

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