get_ui_annotations

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that analytics must be generated first and that data may be sparse or empty for certain study types, which is important for setting expectations. However, it does not mention potential errors or rate limits, but it does provide meaningful behavioral context.

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 well-structured and front-loaded: it begins with a clear purpose, then provides context, a prerequisite warning, and an args section. Every sentence adds value, and the use of a warning emoji for the prerequisite draws attention. It is appropriately sized with no wasted words.

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 simplicity of the tool (one parameter, output schema present), the description covers all necessary aspects: purpose, usage context, prerequisite, and data variability. The output schema documents return values, so the description does not need to explain them. It is complete for an agent to understand when and how to invoke this tool.

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 0%, so the description must compensate. It provides a basic explanation for study_id as 'The unique identifier of the study.' While this adds minimal meaning beyond the parameter name, the parameter is simple, and the description does not explain how to locate or format the ID. This is adequate but not rich.

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 starts with a clear verb+resource: 'Get UI annotation data (heatmaps, click data) for a study.' It further specifies that it is most relevant for prototype or conceptTesting sections and mentions that data may be sparse for voice interviews, which distinguishes it from other study data tools like get_quantitative_data or get_qualitative_insights.

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 the tool is most relevant (prototype/conceptTesting sections) and when it may be less useful (voice interview sections). It also provides a clear prerequisite workflow: check get_analytics_status and call generate_analytics if components are missing. This gives concrete guidance on when to use the tool and what to do before invoking it.

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