detect_quality_issues

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

The description details the types of issues detected and the output structure (column, issue code, severity, explanation), giving a clear picture of behavior. While no annotations are provided, the description is thorough enough to imply a read-only audit operation, though an explicit statement of non-destructiveness would slightly improve transparency.

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: two short paragraphs. The first paragraph states the purpose and lists detected issues; the second adds usage context and output structure. Every sentence adds value, and the information is 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?

The description covers the tool's outputs and usage context, but misses parameter details. Given the complexity of the audit tool and lack of output schema, the description is largely complete, though parameter elucidation would push it to a 5.

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?

With 0% schema description coverage, the description must explain parameters, but it does not mention 'path' or 'max_rows' at all. The purpose is clear, but the user cannot infer what the path parameter represents or how max_rows affects the audit, leaving a significant gap in parameter understanding.

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 runs a focused data-quality audit and returns issues grouped by severity, listing specific issues like duplicate rows, missing columns, and constant columns. This specificity and the explicit differentiation from sibling tools like 'preview_data' and 'profile_dataset' make the purpose unmistakable.

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 usage guidance: 'Use this when the user cares specifically about cleanliness, is preparing data for modeling, or asks "is anything wrong with this data?".' This clearly indicates when to use the tool, though it does not explicitly mention when not to use it or name alternative tools.

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