profile_column

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds substantial behavioral context beyond these: it explains that the profile adapts to column type (numeric/date/text) with specific statistics and warnings, describes the return format as Markdown, and even includes an error message example for invalid input. This goes far beyond annotation 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 well-structured with clear sections (summary, type-specific behavior, Args, Returns, Examples), uses bullet lists for readability, and front-loads the core purpose in the first line. Every sentence earns its place, providing rich detail without fluff or redundancy.

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 tool with only two parameters, the description is thorough: it covers all column type behaviors, return format, error handling, and typical use cases. It also leverages an output schema (though not shown) and annotations, making it fully contextual 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?

The input schema already provides 100% description coverage for both parameters (table and column), including examples and case-insensitivity. The description's Args section essentially repeats this information without adding new meaning. Baseline is 3 due to high schema coverage; the description adds no extra parameter semantics.

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 opens with 'Profile one column: distribution, outliers, coverage gaps, dirty values', which is a specific verb+resource combination that clearly states what the tool does. It further distinguishes itself from sibling tools like describe_table by explicitly saying 'Don't use when: you want the whole schema (use describe_table)' and by focusing on single-column profiling.

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 provides explicit 'Use when' scenarios with concrete example questions ('Is unit_price skewed?', 'Are there missing days in order_date?') and a non-example ('a GROUP BY returned more groups than expected'). It also gives a clear exclusion ('Don't use when: you want the whole schema') and names the alternative (describe_table).

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