check_text_columns

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

With no annotations provided, the description carries the full burden. It discloses multiple behavioral traits: per-column outputs, flags for dedup/mojibake/HTML, a sample size cap on big data, and small output size. It doesn't explicitly state read-only/no side effects, but the analytical nature and output details give good transparency.

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 tightly packed paragraph that front-loads the purpose and then lists outputs and flags. Every sentence provides useful detail without excessive wordiness, though structure could be improved with bullet points.

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?

The description covers the core analysis outputs, flags, and sample cap, and an output schema exists to define return structure. However, it lacks guidance on how to use the `columns` and `sample` parameters (e.g., what null columns means), leaving a gap for effective 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?

The description mentions 'Sample size cap on big data' which indirectly alludes to sampling, but does not explain the `sample` or `columns` parameters at all. With 0% schema description coverage, the agent receives no help interpreting these parameters from the description.

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 uses a specific verb 'Analyze' and resource 'text-heavy string columns' with a concrete threshold (avg length ≥ 20). It lists distinct output metrics (length distribution, vocab, near-duplicate %, content kind, mojibake) which clearly distinguish it from sibling check tools like check_distributions or check_duplicates.

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 clearly implies usage for text-heavy string columns, and the threshold condition (avg length ≥ 20) provides context. However, it doesn't explicitly compare with alternatives or state when not to use it, so it lacks exclusions or alternative references.

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