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set

Adjust PyMOL visualization by assigning a value to any setting, with optional targeting to specific objects or selections.

Instructions

Sets a PyMOL setting to a specified value

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
valueYesThe value as a string, e.g. "0.5", "1", "black".
settingYesPyMOL setting name, e.g. "cartoon_transparency", "ray_shadow", "sphere_scale". Hundreds exist; see the PyMOL settings reference.
selectionNoLimit the setting to this object or selection. Omit to set it globally.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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. It only states 'Sets a PyMOL setting' but does not disclose that settings can be scoped to a selection, that values are strings, or that changes may affect rendering/session state. The side effects and scope of the operation are not addressed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, short sentence with no redundancy or filler. It is front-loaded and immediately conveys the tool's essence, which is ideal for a simple setter.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a basic setter, the description is minimally adequate: it covers the action and the object, and the schema covers parameters. However, it omits nuances like global-vs-selection scoping, case sensitivity of setting names, and whether the operation returns a confirmation. As an output schema exists, full return value description is not required, but some contextual guidance would improve it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema provides 100% coverage for all three parameters ('setting', 'value', 'selection') with clear descriptions and examples. The description adds no additional parameter-level meaning, so the baseline of 3 applies because the schema already documents everything.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Sets') and the resource ('a PyMOL setting to a specified value'), which makes the core purpose understandable. However, it does not differentiate from siblings like 'execute_pymol_command' which could also be used to set settings, and it lacks examples of typical settings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no guidance on when to use this tool versus alternatives. It doesn't mention that this is the preferred way to modify individual settings, nor does it point to 'execute_pymol_command' for arbitrary PyMOL commands or 'show'/'hide' for common visual toggles.

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

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