data_diff

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds context about the output being 'compact' and that it 'returns added, removed, and changed counts and samples', which goes beyond annotations. There is no contradiction, and the description adds value by clarifying the behavioral output characteristics.

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, compact sentence that front-loads the purpose and includes the key optional parameter (key fields) and the output nature. Every phrase earns its place, with no redundancy or fluff. This is ideal conciseness.

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?

The tool has 6 parameters including complex source objects (PathSource/InlineSource) and a limits model, but the description does not mention that sources can be paths or inline, nor the limits concept. However, the schema provides rich detail on these, and the output is described. Given no output schema, the description covers the return format adequately. The main gap is not explaining how sources are specified, but the schema covers that, so a moderate score is fair.

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 schema has 0% description coverage, but the description explicitly mentions 'stable key fields' matching the key_fields parameter and 'compact counts and samples' relating to sample_rows. However, it does not explain the left/right source objects, limits, or workspace parameters. The description adds some meaning but not enough to fully compensate for low coverage; baseline 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 'Compare two structured datasets by schema and rows' with 'optional stable key fields', and specifies the output as 'compact added, removed, and changed counts and samples'. This is a specific verb+resource combination that distinguishes it from siblings like data_transform (transforms), data_inspect (inspects), and data_validate (validates).

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 implies usage when two datasets need comparison, but it does not explicitly mention when not to use it or name alternatives. Sibling tools are obvious from their names, but the description could have added context about when to choose this over data_inspect or data_validate. Still, the purpose is clear enough that a moderate score is warranted.

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