explain_data

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

No annotations are provided, so the description carries the full transparency burden. It does disclose that the tool 'returns natural-language descriptions' and screening results, implying a read-only analytic operation, but it never explicitly states non-mutating behavior or any access/performance considerations. Adequate but not detailed.

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 sentences, front-loaded with a clear purpose statement, followed by a concise explanation of return content and use case. Every sentence earns its place and there is no redundant or vague wording.

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 fully described input schema and presence of an output schema, the description supplies the necessary context for when and why to use the tool. It is complete for typical agent selection, though it omits any potential limitations or prerequisites (e.g., column types, permissions).

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 descriptions for all 5 parameters (100% coverage), so the baseline is 3. The tool description adds little parameter-specific meaning beyond the schema; it only ties 'target_variable' to the general concept of driving a target, which does not materially improve parameter understanding.

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?

Description clearly states a specific verb ('Explain') plus resource and scope ('a column of a CAS table in relation to the other columns'). It also names SAS Insights and the output types (natural-language descriptions, outliers, variable-screening results), making it distinct from sibling data and ML tools.

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?

Explicitly tells when to use the tool: 'before exploring or modelling the data' to understand which variables drive a target. It does not name alternatives or provide when-not-to-use conditions, so it falls just 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.