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

Annotations already declare a safe read (readOnly, idempotent, non-destructive), so the main added value is the disclosed mode switch: before insights exist (aggregated=false) it returns transcript excerpts rather than summary/themes. That is genuine behavioral context an agent can't get from annotations, though pagination or size limits are not addressed.

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 dense sentences with no filler, and the return contents are front-loaded ahead of the mode caveat. Slightly list-heavy, but every clause carries information.

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?

With an output schema present the enumerated return fields are somewhat redundant, but the tool is a read on structured interviews and the description covers the one non-obvious behavior (pre-insights responses). An agent has enough to call it correctly.

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 coverage is 100% and each parameter is documented, including the analysisVersionId stability rationale, so the description only alludes to the question reference implicitly. No additional parameter meaning is supplied beyond the schema.

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?

States a specific verb and resource ('Read responses to one discussion-guide question of an AI-moderated study') and enumerates what it returns, which separates it from list-style siblings. It doesn't name the closest alternatives (get_interview_insights, get_interview_transcript, get_question_responses), so the agent must infer boundaries from scope alone.

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?

Usage is implied by the aggregated=false branch note, which tells the agent the tool changes shape depending on study state. There is no explicit when-to-use/when-not or named alternative, leaving the agent to reason about the sibling set on its own.

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