Plan Analysis

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

Annotations already mark it read-only, but the description adds substantial behavioral detail: it lists assumptions to check, insists on effect sizes with confidence intervals over bare p-values, flags p-hacking/HARKing/multiplicity risks, and handles missing data. It clarifies that the tool produces a strategy/plan rather than performing actual analysis, which is valuable beyond annotations.

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 three sentences, front-loaded with the core purpose. The first sentence states the main function, the second elaborates on quantitative and qualitative modes, and the third gives usage timing. Every sentence earns its place with no redundancy.

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 description is fairly complete for a planning tool with 3 parameters and no output schema. It covers both quantitative and qualitative branches, mentions key rigor aspects, and provides temporal context. However, it does not explicitly describe the output format (e.g., a structured plan, a list of assumptions), which would be helpful given the lack of an output schema.

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 100%, so baseline is 3. The description adds context by explaining how the 'question' drives the test/model family and how 'variables' (key variable types) are matched, and it mentions that for qualitative data 'variables' describes the nature of the data. This goes beyond the schema's individual field descriptions.

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 opens with a specific verb+resource: 'Recommend an analysis strategy and keep the inference honest.' It clearly distinguishes from sibling tools by detailing quantitative and qualitative functions (matching test/model families, listing assumptions, flagging p-hacking) and by positioning itself 'after design-study, before collecting or analysing data.'

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 explicitly states when to use this tool: 'Use after design-study, before collecting or analysing data.' This gives clear contextual timing, though it does not name specific alternative tools for when not to use it, which is a minor gap.

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