get_qna_analysis

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

Since no annotations are provided, the description carries the full burden. It discloses the prerequisite that analytics must already be generated, which is a critical behavioral constraint not visible from the schema. It also implies a read-only operation and describes the output organization. However, it does not discuss failure modes or authorization requirements, leaving a small gap.

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 concise and well-structured: a clear purpose sentence, a usage sentence, a prerequisite warning, and the argument definition. No filler or repeated schema content, and all critical information is front-loaded.

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 single-parameter read tool with an output schema, the description covers purpose, usage, prerequisites, and parameter semantics. The output schema handles return value details, so nothing essential is missing. It also differentiates from sibling analytics tools through the 'per-participant' focus.

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 schema has zero description coverage (0%), but the description defines the only parameter as 'the unique identifier of the study,' fully clarifying its meaning. This compensates for the schema gap, though the explanation is minimal and could mention format/origin.

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 uses a specific verb ('Get') and clearly defines the resource ('per-participant Q&A analysis') with structural detail ('organized by subtopic across all study sections'). This distinguishes it from sibling analytics tools like get_quantitative_data or get_analytics_summary, which focus on aggregate or other data types.

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: 'Use this when the user wants to compare how different participants answered the same questions, or to see individual-level responses side by side.' It also provides a workflow prerequisite (call get_analytics_status, then generate_analytics if needed), which is actionable guidance beyond simple use cases.

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