describe_variable

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

With no annotations, the description carries the full burden and does well by disclosing both the output contents and the special behavior of flagging refused/don't-know codes. It stops short of explicitly stating that the operation is read-only, but 'describe' strongly implies 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 sentences with no filler: the first defines the tool's output, the second adds a useful behavioral detail. Every word 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?

For a simple 3-argument tool, the description conveys the core return value and a meaningful behavior. It is incomplete on parameter semantics and does not explain how to source the table/cycle/variable values, but it is adequate as a minimum viable description.

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% and the description does not mention variable, table, or cycle at all. The agent receives no help interpreting what these three required parameters mean or where their values come from.

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 exactly what the tool returns: a codebook entry (label, question text, target population, value codes) plus an observed distribution. Although it lacks an explicit verb, the resource and scope are specific enough to distinguish it from describe_dataset and search_variables.

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?

No guidance is given on when to use this tool versus alternatives like search_variables or describe_dataset. The intended context—inspecting a variable before analysis—is only implied by the tool name and content, not stated.

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