survey_mean

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

With no annotations, the description carries the full behavioral burden and meets it: it discloses design-correct error computation, the risk of understated SEs, multi-stage plausible-value expansion, two accepted design specifications, and the fact that output separates sampling versus measurement error. It also states method defaults for JK2 and Fay, which is exactly the behavioral nuance an agent needs beyond the schema.

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 front-loaded and every sentence adds relevant guidance with no filler. It is dense rather than concise, packing three distinct topics (when-to-use, plausible values, replicate-weight designs) into one paragraph, but the complexity of the tool justifies the length.

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 13-parameter tool with no output schema and no annotations, the description covers the crucial decision points: sampling design, plausible values, replicate-weight specification, and method defaults. It still relies on the schema for obvious details like `by`, `digits`, and `fay`, but the high-risk ambiguity areas are all resolved.

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?

Although schema coverage is 100%, the description adds substantial meaning: `pv_prefix='BSMMAT'` expands to BSMMAT01..05, `pv_pattern='PV{i}MATH'` expands to PV1MATH..., `replicate_weights` accepts either lists or prefixes, and `jkzone` + `jkrep` builds JK2 replicates automatically. This is essential operational detail that the bare schema fields do not convey.

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 opening sentence names a specific verb, resource, and key differentiator: 'Means for complex survey data, with design-correct standard errors.' It also explicitly contrasts with `describe`, which lets an agent immediately know this is the survey-aware mean tool rather than a generic descriptive tool.

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 says to use this tool 'instead of `describe`' for clustered or stratified samples and explains why the ordinary standard error is inadequate. It also gives concrete conditions for plausible values versus single scores and how to supply replicate weights, providing clear when-to-use and how-to-proceed guidance.

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