data_cleaner

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'intelligent detection' but doesn't explain what this entails operationally—whether it modifies files in-place, creates backups, has performance characteristics, or requires specific permissions. For a batch processing tool with mutation implications, this is a significant gap.

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 a single, efficient sentence that immediately communicates the core functionality without unnecessary words. It's appropriately sized and front-loaded with the essential information.

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?

For a data cleaning tool with 4 parameters, no annotations, and no output schema, the description is insufficient. It doesn't address critical behavioral aspects like whether changes are destructive, what formats are supported beyond CSV/Excel, error handling, or output expectations. The context signals indicate this is a moderately complex tool that needs more complete documentation.

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 already documents all four parameters thoroughly. The description adds no additional meaning about parameters beyond what's in the schema—it doesn't explain how operations interact, what 'intelligent detection' means for parameter selection, or provide usage examples.

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 'Batch data cleaning operations' with 'intelligent detection of common data quality issues', specifying both the verb (cleaning) and resource (data). However, it doesn't explicitly differentiate from siblings like 'validate_data_consistency' or 'find_duplicates' which might overlap in data quality domains.

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. With siblings like 'validate_data_consistency', 'find_duplicates', and 'smart_data_analysis' that might handle similar data quality tasks, the description offers no context about appropriate use cases or exclusions.

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