Test a research pipeline on placebo data

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

Annotations are non-readonly, non-destructive, non-idempotent, and closed-world, and the description explains why: 'plan writes placebo files' on your data. It also discloses the key interpretive limit ('does not show the edge survives costs, capacity or the future'), which goes well beyond the annotation set.

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?

It is front-loaded with a framing question and every clause carries content, but the heavy nested parentheses make it denser and harder to parse than necessary. Still, there is no filler sentence.

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 10-parameter, two-mode tool with an output schema (so return values need no explanation), the description conveys the conceptual workflow and the limits of the result. The remaining gap is that several non-enum parameters are left to the schema, but nothing critical to correct invocation 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?

Schema description coverage is only 60%, and most parameter meaning (real, placebos default 19 up to 199, columns, data_file) lives in the schema descriptions rather than the prose. The prose adds conceptual context (permuted returns, p-value counting every choice) but says nothing about method, seed, or lower_is_better, so it does not fully compensate for the coverage gap.

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 concrete verb and resource: it writes placebo files with returns in random orders, has you run the pipeline on real and placebo data, and compares them for a p-value. This is far more specific than the title. It does not, however, name or contrast the sibling validate_* tools, so sibling differentiation is left to inference.

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 implies the workflow (plan writes placebos, then run the pipeline, then compare) and frames the use case as 'Does your pipeline find edges in noise?'. But there is no explicit when-to-use/when-not guidance versus the many sibling validation tools, and the action flow is only implied rather than spelled out.

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