create_study

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

With no annotations provided, the description carries full disclosure responsibility. It transparently warns that the operation takes 30-90 seconds, instructs the agent to set user expectations, and describes the automatic generation steps and the requirement to present a study summary afterward. It does not mention error handling or permissions, but for a creation action these are less critical.

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 long but well-structured with headings, bullet lists, and a front-loaded latency warning. Each section earns its place, though there is some redundancy between the 'For best results' bullet list and the Args explanation, so it could be slightly tighter.

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 has only one parameter but complex generative behavior, the description covers input requirements, the generation process, latency, and the agent's post-creation action. Since an output schema exists, return values need not be described here. This is complete enough for an agent to invoke the tool 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 0% and the parameter is just a generic string, but the description compensates thoroughly. It explains the 'description' parameter should be a natural language description including product, target users, research goals, tasks, and prototype URLs. This gives the agent concrete guidance on what to collect from the user, far exceeding the schema's bare type definition.

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 verb+resource ('Create a new UX research study using AI-powered generation') and reinforces this by stating it is the PRIMARY way to create studies. This distinguishes it from sibling tools such as get_study, update_study_name, and copy_study, which manage or transform existing studies.

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 explicitly says 'This is the PRIMARY way to create studies' and provides detailed guidance on what to include in the description for best results. However, it does not explicitly list alternatives or when not to use the tool, though the creation context is clear relative to the read/update/delete siblings.

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