compare_datasets

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

No annotations are provided, so the description carries the full burden. It indicates a read-style operation (comparison) without stating side effects or permissions. The description is adequate but does not explicitly disclose whether the tool modifies data, requires authentication, or has rate limits. For a safe comparison tool, it is minimally sufficient.

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 extremely concise, containing only three lines. The first line states the core purpose, followed by usage examples. Every sentence adds value with no redundancy. Well-structured and front-loaded.

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 that an output schema exists (not shown), the description does not need to explain return values. It covers the tool's purpose, parameters, and usage context. For a tool with only two string parameters and no nested objects, the description is sufficiently complete for an AI agent to decide when to use it.

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 100%, with both parameters having basic descriptions ('Name of the first dataset', etc.). The tool description adds context about what the comparison entails (shapes, distributions, etc.) but does not add specific constraints or formatting for the parameter values. Baseline score of 3 is appropriate.

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 it compares two datasets side-by-side, covering shapes, columns, distributions, and statistical differences. This is specific and goes beyond a mere verb. However, it does not explicitly differentiate from sibling tools like 'statistical_test' or 'data_quality_report', which may have overlapping purposes.

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 concrete examples of when to use: comparing train/test splits, before/after transformations, or different time periods. This provides good context. However, it lacks 'when not to use' or explicit alternatives to other sibling tools.

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