altloc_view

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

The description explains what the tool displays (all altloc groups with distinct colors and occupancy labels) and why that matters, but does not disclose that it modifies the current PyMOL display state (colors/labels) or any potential side effects such as overwriting existing visual settings. With no annotations, the description carries the full burden and only partially discloses behavior.

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 first sentence is front-loaded with the core function. The subsequent rationale is useful for context but could be slightly more concise; still, every sentence adds value and the length is appropriate for the tool's purpose.

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 simple two-parameter tool, the description covers the purpose, rationale, and core behavior. It lacks explicit mention of side effects or prerequisites (e.g., object must have altloc groups), but the overall context is sufficient. The output schema reduces the need to describe return values.

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?

Both parameters are already fully described in the schema with 100% coverage. The description reiterates the labeling behavior but adds little meaning beyond the schema, such as clarifying the role of obj_name or label. This is baseline for high schema coverage.

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 specific verb and resource: 'Show every altloc group at once, one colour per group, occupancies labelled.' This clearly identifies the tool's function and distinguishes it from PyMOL's default single-conformer display, as explained in the following rationale.

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 second paragraph explains when this tool is valuable—when a multiconformer model contains discrete alternatives that PyMOL's default view hides. While it does not name alternative tools or explicit exclusions, it gives clear context about the problem it solves.

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