data_quality_report

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

With no annotations provided, the description carries the full burden. It states it returns a narrative report with diagnostic plots, which implies a non-destructive read operation. However, it does not explicitly state whether it modifies any state (e.g., creates a plot object), nor does it mention performance implications or side effects. The description is adequate but minimal.

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 sentences that front-load the core purpose and immediately detail the analyses performed. Every sentence provides essential information with no filler or repetition.

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 low complexity (3 parameters, no nested objects) and the presence of an output schema, the description is largely complete. It covers the tool's purpose, the scope of analysis, and the output format. The minor omission of prerequisites (e.g., dataset must be loaded) is mitigated by the parameter name 'dataset_name'.

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 baseline is 3. The description adds overall context by listing what the tool analyzes (missing values, outliers, etc.), but does not add specific parameter details beyond what the schema already provides for 'dataset_name', 'outlier_method', and 'outlier_threshold'. The added value is marginal.

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 verb 'generate' and the resource 'data quality report with visualizations'. It lists specific analyses (missing values, outliers, data types, etc.), making the tool's purpose distinct from siblings like 'analyze_data' or 'statistical_test'. However, lacking explicit sibling differentiation prevents a 5.

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?

No guidance is provided on when to use this tool versus alternatives such as 'analyze_data', 'auto_eda', or 'statistical_test'. The description does not include any contextual hints about prerequisites, best practices, or exclusion criteria.

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