get_column_profile

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

No annotations are present, so the description carries the burden. It discloses conditional numeric behavior ('if numeric'), caps value frequencies at top 10, and includes null/unique counts. This is substantive and goes beyond a generic 'analyze' statement, though it does not mention error handling or whether the dataframe is mutated (implied read-only).

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 front-loaded with the most important output list, then moves to use cases and a concrete example. Every sentence adds value without redundancy; it is compact and scannable.

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 an output schema exists (so return format is covered), the description provides sufficient context for a single-column analysis tool: what it computes, when to use it, and how to invoke it. The only notable gap is the undocumented df_name parameter, which prevents a perfect score.

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 0%, so the description must compensate. It clarifies the 'column' parameter through the example get_column_profile(column="Revenue") and the 'one column' wording, but it does not explain the optional df_name parameter beyond the schema's title/default. This is partial compensation, not complete.

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 'Detailed single-column analysis' and enumerates specific outputs (dtype, nulls, unique count, statistics, top 10 frequencies), making the tool's purpose and scope unambiguous. It clearly differentiates from sibling tools like get_statistics and get_unique_values by focusing on a single-column deep-dive.

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?

States explicit trigger: 'Use when quality_report flags something' and connects output to follow-up action: 'Provides skewness for deciding if log_transform is needed.' It lacks an explicit when-not or named alternatives, but the use context is clear.

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