Design Study

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

With readOnlyHint=true already signaling a read-only operation, the description adds meaningful behavioral detail by outlining the returned scaffold: question-type classification, candidate designs, validity threat checklist, sampling considerations, and recommendation. It does not contradict annotations and goes beyond what annotations alone convey.

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 two sentences, front-loaded with the primary purpose and followed by a compact list of return components. Every sentence earns its place with no redundancy or filler.

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 tool's complexity and lack of an output schema, the description thoroughly explains what the tool returns (the scaffold components) and when to use it (after scope-question). It provides enough detail for an agent to understand the tool's role and expected outputs.

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 parameters are already well documented. The description adds minimal semantic value beyond the schema, merely restating 'research question and its constraints' and indicating that the question should be sharpened via scope-question. This meets the baseline for high schema coverage but does not exceed it.

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 clearly states the tool's function: 'Recommend a fit-for-purpose study design for a research question and its constraints, then surface the validity threats it must address.' It specifies the verb (recommend), the resource (study design), and the scope (research question + constraints), making it distinct from sibling tools like appraise-evidence or plan-analysis.

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 provides explicit usage context: 'Use after scope-question.' This clearly positions the tool in a workflow. However, it does not mention when not to use it or provide alternative tool names, so it falls 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.