get-dataset-examples

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

The description discloses the return structure (dataset ID, version ID, array of examples) and provides an example. However, it does not mention auth requirements, rate limits, or potential side effects. Since annotations are absent, the description partially fulfills the burden.

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 concise with three sentences covering purpose, example, and return format. The example with a specific ID adds some overhead but does not detract significantly from clarity.

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 4 parameters with no schema descriptions and no output schema, the description should explain parameters and possibly differentiate from siblings. It partially describes the return but leaves parameter semantics unaddressed.

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%. The tool description only mentions a specific dataset ID in the example but does not explain the meaning of any of the four parameters (dataset_id, dataset_name, version_id, splits). This provides no useful semantics beyond the schema itself.

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 'Get examples from a dataset' and elaborates on what examples are (input/output/metadata). It includes an example usage and expected return format, making the tool's purpose specific and distinct from siblings like 'add-dataset-examples'.

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 does not explicitly provide when-to-use or when-not-to-use guidance, nor does it mention alternative tools. Usage is only implied by the example, leaving an agent without clear context for selection.

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