clean_data

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'return results' and an optional output_path, but does not disclose whether the original file is modified, what 'fill_nulls' fills with, or any potential destructive side effects. This is a significant gap for a tool that can alter data.

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 and well-structured, with a clear opening sentence and a compact parameter list. No unnecessary words or repetition; every sentence serves a purpose.

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 parameters and purpose adequately, and an output schema exists to define return values. However, it lacks usage guidance (when vs. alternatives) and behavioral safety details (side effects, mutation), leaving the tool only partially complete for an agent to invoke with full confidence.

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 0%, yet the description fully compensates by explaining each parameter: dataset_path is CSV format, operations lists allowed values, and output_path is optional. This adds crucial meaning beyond the raw schema, enabling correct invocation.

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 performs data cleaning operations, enumerating specific operations like 'remove_nulls' and 'remove_duplicates'. This verb + resource combination is specific and implicitly distinguishes it from sibling tools (analyze_dataset, statistical_summary) which focus on analysis, not cleaning.

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 implies usage when data cleaning is needed but provides no explicit guidance on when to choose this tool over alternatives. No when-not-to-use conditions or references to sibling tools are given, leaving the agent to infer suitability.

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