data_inspect

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

With no annotations, the description carries the behavioral burden. It is transparent about what is returned and about the tool's role in guiding analysis. It doesn't explicitly state the operation is read-only, but 'Inspect' and the output list strongly imply no side effects.

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?

Two tight sentences front-load the output inventory and then provide actionable guidance. No filler or redundancy; every clause earns its place.

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 tool is simple, the schema fully documents the parameters, and the description covers the return surface (dimensions, types, missing/distinct counts, examples, first rows) in the absence of an output schema. Nothing an agent needs to decide whether to call it is missing.

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?

All three parameters already have clear descriptions in the schema (data name, row count with bounds, optional variable restriction), so the description doesn't need to add parameter detail. It doesn't provide any extra semantics beyond what the schema already states, matching the 100% coverage baseline.

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 states a clear verb and resource ('Inspect a dataset') and enumerates specific outputs (dimensions, per-column type, missing counts, distinct counts, example values, first rows). It doesn't explicitly contrast itself with a sibling like describe, but the emphasis on column types and factors gives it a distinct identity.

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?

It gives an explicit 'when' directive: 'Run this before choosing an analysis,' and explains why (numeric vs factor columns determine whether a test will work). It doesn't name alternatives or say when not to use it, so it stops short of a 5.

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