get_dataset_info

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses the output content (metadata) but does not mention whether the operation is read-only (implied by 'get'), or any side effects or error conditions. For a simple metadata retrieval tool, this is acceptable but not enriched beyond the obvious.

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 a single, well-structured sentence that front-loads the core function ('Returns high-level metadata'), then lists specific items, and ends with a usage recommendation. No redundant words or filler.

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?

Given the zero-parameter signature and the presence of an output schema (which covers return format), the description provides all necessary context: what the tool does, what it returns, and when to use it. Nothing an agent needs to invoke it correctly is missing.

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?

There are zero parameters, and the schema has no properties, so schema coverage is trivially 100%. Per the rubric, the baseline for 0 parameters is 4. The description adds value by explicitly enumerating what the returned metadata includes, which is not captured by the 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 clearly states what the tool does: returns high-level metadata for the Telco Customer Churn dataset, specifying the exact items included (available analytics table, row count, column count, target variable). It is distinct from sibling tools like list_columns and describe_column, which operate at a different granularity.

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 includes an explicit usage directive: 'Use this tool before deeper analysis when dataset context is unknown.' This provides clear context on when to invoke it, though it does not explicitly mention alternatives or when not to use it. Still, the guidance is actionable and sufficient for an agent to decide.

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