profile_dataset

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

With no annotations, the description effectively discloses key behaviors: no modification, PII column name scan (non-blocking, annotated), and return format. It does not cover every edge case (e.g., large files, permissions) but is sufficient for an agent to understand safe usage.

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 well-structured with a clear hierarchy: purpose, supported formats, behavioral note, parameter descriptions, return value list. It is front-loaded with the most important information. Some redundancy exists (e.g., 'never modifies' is repeated in implication), but overall concise.

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 and the presence of an output schema (not shown but noted), the description covers all critical aspects: inputs, outputs, side effects, supported formats, and PII handling. It is complete enough for an agent to select and invoke correctly.

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%, so the description must compensate. It explains 'path' as absolute local path and 'sample_rows' as controlling data sample inclusion. This adds meaning beyond the schema's type/default, though it could clarify path validation.

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 profiles a tabular dataset, listing outputs like shape, dtypes, null %, unique counts, distributions. It distinguishes from siblings like clean_dataset by explicitly stating 'Never modifies the source file'.

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 use for exploring dataset structure and quality but does not explicitly state when to use versus alternatives (e.g., clean_dataset, suggest_cleaning). It lists supported file formats, providing some context, but lacks direct guidance on when not to use.

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