detect_quality_issues

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

No annotations, but the description details the types of issues detected and the output structure (column, issue code, severity, explanation). It does not cover performance or modification (non-destructive implied), but it is transparent about the behavior.

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, front-loaded with the summary, then lists issues, then usage guidance. Every sentence adds value with no 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 the complexity (many issue types) and lack of output schema, the description adequately covers the purpose, input, and output format. It is missing parameter details, but overall it is fairly complete for the tool's scope.

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%, meaning the description does not explain the parameters (path, max_rows). The user cannot infer what 'path' refers to or how 'max_rows' affects the audit. This is a critical gap.

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 uses a specific verb ('Run a focused data-quality audit') and resource ('return issues grouped by severity'), and lists specific checks (duplicate rows, missing columns, etc.), clearly distinguishing it from sibling tools like profile_dataset or preview_data.

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 provides explicit use cases: 'when the user cares specifically about cleanliness, is preparing data for modeling, or asks 'is anything wrong with this data?'. It does not explicitly mention when not to use or alternatives, but the guidance is clear and helpful.

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