compare_datasets

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

No annotations are provided, so the description carries the burden. It explains what the tool reports but does not disclose limitations, performance characteristics, or side effects (e.g., whether it loads entire files into memory).

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 three sentences: purpose, output details, use cases. It is front-loaded with the key verb and resource, and every sentence adds value without redundancy.

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 no output schema, the description explains the return values in reasonable detail (deltas, column changes, etc.). However, it does not mention how max_rows affects the results or the output format (e.g., a structured table).

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?

The input schema has 0% description coverage. The description implies path_a and path_b are the files to diff but does not explain the max_rows parameter or provide details on expected formats or constraints.

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 purpose: 'Diff two tabular files' and lists specific output metrics (row-count delta, column changes, dtype changes, null rates, means). This distinguishes it from sibling tools like column_stats or preview_data which focus on single datasets.

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?

Explicitly provides use cases: comparing snapshots, validating data pipeline baselines, checking transformations. While it doesn't mention when not to use it or name alternatives, the intended contexts are clear.

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