add_study

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

With no annotations, the description carries the full burden of behavioral disclosure, but it only restates the intended operation. It does not mention side effects on the model, return behavior, whether adding a study replaces an existing one, or any permissions or prerequisites.

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 a single front-loaded sentence with no filler. It conveys the core purpose and the most important value constraint efficiently.

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 three parameters, a free-form nested settings object, and no annotations, the description is too thin. It does not explain tag or settings semantics, when to call it, or what behavioral effects to expect, leaving important invocation decisions to the agent.

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 description coverage is 0%, and the description only clarifies the allowed values for study_type. The required tag parameter and the settings object remain unexplained, and since settings allows arbitrary additional properties, the agent gets no guidance on what keys or values are expected.

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 names a concrete action and resource ('Add a study') and enumerates the valid study types (Stationary|TimeDependent|Parametric|Eigenfrequency), so the agent knows exactly what this tool does. It is clearly distinguishable from sibling tools like add_physics or add_material.

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 gives no explicit guidance on when to use this tool versus alternatives, no prerequisites, and no context such as needing a loaded model before adding a study. Usage is only weakly implied by the tool name and resource type.

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