check_data_quality

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 mentions 'predefined or custom rules' and returns a 'comprehensive quality assessment,' but lacks details on behavioral traits: e.g., whether it's read-only (likely, but not stated), performance implications, data sources, or error handling. The description adds minimal context beyond the basic operation.

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 appropriately concise with two sentences: one stating the purpose and one describing the return. It's front-loaded with the core function. No wasted words, though it could be slightly more structured (e.g., separating usage notes).

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 tool's complexity (multiple rule types) and rich input schema (100% coverage) with an output schema (implied by 'Returns'), the description is minimally adequate. It covers the basic purpose and return type but lacks context on when to use, behavioral details, or integration with siblings. With no annotations, it should do more to compensate.

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%, so the schema fully documents the single parameter 'rules' with its types and defaults. The description adds no parameter semantics beyond what's in the schema (e.g., it doesn't explain rule interactions or provide examples). Baseline 3 is appropriate as the schema does the heavy lifting.

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: 'Check data quality based on predefined or custom rules.' It specifies the verb ('check') and resource ('data quality'), and distinguishes it from siblings like 'detect_outliers' or 'find_anomalies' by focusing on comprehensive rule-based assessment. However, it doesn't explicitly differentiate from 'validate_schema' or 'profile_data', which could be related.

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 no guidance on when to use this tool versus alternatives. It doesn't mention when to prefer this over siblings like 'detect_outliers' (which might handle a subset of checks) or 'validate_schema' (which might focus on structural validation). No context, exclusions, or prerequisites are stated.

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