plddt_view

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

With no annotations, the description carries the full burden. It discloses the color palette thresholds, the storage of pLDDT in the B-factor column, and the interpretive caution for regions below 70. It does not mention side effects beyond coloring, but this is likely a single-purpose visualization operation.

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 front-loads the purpose; subsequent sentences provide essential details (palette, threshold interpretation, and comparison to alternatives). Every sentence adds unique value with no filler.

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 single-parameter coloring tool, the description covers what it does, the exact color thresholds, and when it should replace competing tools. The presence of an output schema means return values need not be described. It is complete for the intended use.

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 100% with obj_name described as 'PyMOL object name (e.g. "AF_P69905").' The description adds no new parameter-specific details beyond reinforcing the AlphaFold context, so the baseline of 3 for full schema coverage applies.

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: 'Colors an AlphaFold model by pLDDT confidence, using the official palette.' It clearly distinguishes from siblings by explicitly naming bfactor_view and putty_view as tools that handle these models incorrectly.

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 explicit when-to-use guidance: 'Use this instead for predicted models.' It also explains why alternatives fail ('they assume low = rigid, whereas low pLDDT = low confidence'), establishing clear exclusion criteria.

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