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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds useful behavioral context that the tool returns a sample row and column metadata, and that the full dataset list is scoped to what data_query can access. No hidden side effects or contradictions are present.

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 no-argument case is stated first, the argument case second, and each sentence earns its place by conveying a distinct operational mode. The structure is easy to parse and highly efficient.

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 read-only metadata tool with one optional parameter and no output schema, the description covers all essential information: invocation modes, what each mode returns, and the scope of available datasets. No critical gap remains for an agent to call it correctly.

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 0%, so the description carries the full burden of explaining the optional dataset_key parameter. It explicitly maps each parameter state to its behavior: absent means list all datasets, present means describe that dataset. This fully compensates for the schema's lack of parameter descriptions.

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 with a specific verb: list available datasets when no arguments, or describe columns, column descriptions, and a sample row when a dataset_key is provided. It distinguishes itself from data_query by positioning itself as the metadata/schema exploration tool for datasets used by data_query.

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 conditional usage guidance: use without arguments to enumerate datasets, or with dataset_key to inspect schema details. It does not explicitly discuss when not to use it or name alternatives, but the two modes are clearly differentiated and the context is obvious enough for an agent to select it appropriately.

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