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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world behavior, so the safety profile is covered. The description adds useful context about the returned data (research objective, goals, discussion guide, interviews, insights status) and notes that the refs it returns are used by other interview tools, aiding interoperability understanding. The output schema covers detailed returns, so this is not a 5.

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 sentences, both front-loaded and information-dense. The first sentence establishes usage and exclusions; the second enumerates the return contents. No filler, no repetition.

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 presence of an output schema, full annotation coverage, and a clear one-parameter schema, the description provides everything an agent needs: when to use it, what it returns at a high level, and how it relates to other tools. No critical gaps remain.

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?

The input schema has 100% description coverage for the single parameter (testId), including its pattern and the requirement that it refer to an AI-moderated test. The description does not add any parameter-specific detail beyond what the schema already provides, so the baseline of 3 applies.

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 clear directive ('Start here for an AI-moderated interview study') and specifies the resource (AI-moderated interview study) and its distinguishing type. It explicitly separates this tool from other result tools, and the return-content list makes the purpose unambiguous.

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 states when to use this tool ('Start here for an AI-moderated interview study') and when not to use alternatives ('other result tools do not apply to it'). It also tells the agent how to verify the correct test type via list_tests/get_test, leaving little to inference.

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