interpret_column_data

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

With no annotations provided, the description fully discloses key behaviors: it scans the whole file, reports dtype/total/null/unique counts, value frequencies sorted most-common first, and caps frequencies at 200 distinct values. It does not mention performance implications but covers essential behavioral traits.

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 with two paragraphs. The first sentence immediately states the purpose. The second paragraph provides essential details without superfluous words. Every sentence adds value.

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 has three parameters and an output schema, the description explains the output components (dtype, counts, frequencies) and behavior (full scan, 200 cap). It adequately informs an AI agent for correct invocation.

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 100%, so all parameters are described. The description adds value by mentioning file format support (CSV/TSV/Excel), sheet_name applicability, and the cap on distinct values. This enriches the schema's bare descriptions.

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 returns 'complete value distribution' of columns, specifying verb 'return' and resource 'value distribution'. It distinguishes itself from sibling read_metadata by noting it scans the whole file rather than a sample, making the purpose unambiguous.

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 explicitly states this tool is 'ideal for understanding categorical columns' and contrasts with read_metadata ('Unlike read_metadata, this scans the whole file'). It does not explicitly list when not to use or name other alternatives, but the context is clear.

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