advanced_data_preprocessing

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

No annotations exist, so the description carries the burden. It discloses the output format (markdown report with parameters and preview) and mentions data structure handling (flattening from objects). However, it does not discuss side effects, permissions, or whether operations are destructive (though transformations are non-destructive).

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?

Two sentences: first covers purpose and transformations, second covers output and sibling alternative. 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 2 parameters and no output schema, the description covers the tool's function, input types, output format, and an alternative tool. It does not discuss performance or limits (though schema has maxItems), but for a simple tool this is adequate.

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 coverage is 100%, baseline 3. The description adds meaning: explains that missing_value_handling drops invalid entries, outlier_detection uses IQR fences, normalization scales to [0,1], standardization gives z-scores, and data can be flattened from objects. This goes beyond the schema.

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 it transforms numeric series for modeling, lists four specific transformations (min-max normalization, z-score standardization, missing-value handling, IQR outlier detection), and specifies the output is a markdown report with parameters and preview. It also distinguishes from analyze_dataset.

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?

Explicitly directs to use analyze_dataset for describing data without changing it, providing a clear alternative. While it doesn't specify when not to use this tool beyond that, the guidance is sufficient.

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