plddt_view

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

Beyond the annotations (readOnlyHint=false, idempotentHint=true, destructiveHint=false), it discloses the critical domain behavior: pLDDT lives in the B-factor column, and regions below 70 should be read as 'the model does not know' rather than a flexible loop. That is genuine interpretive context not encoded anywhere else. It does not state that existing colors are overwritten or whether the coloring is reversible, which keeps it from a 5.

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?

Front-loaded with the action and palette, then the legend, then the sibling rationale — a logical order with no filler sentences. The four-tier color enumeration is somewhat long for a coloring tool, but it is the mechanism by which an agent interprets the result, so it largely earns its place.

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 one-parameter coloring tool with full annotations and an output schema, nothing an agent needs is missing: what it does, which models it applies to, which alternatives to avoid, and how to interpret the resulting colors. Return values are covered by the output schema, so their absence from the description is not a gap.

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% for the single obj_name parameter, which already documents the PyMOL object name with an example. The description adds only the constraint that the object should be an AlphaFold/predicted model, which is useful but does not extend parameter semantics meaningfully. Baseline 3 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?

States a specific verb and resource: 'Colors an AlphaFold model by pLDDT confidence, using the official palette.' It also names the sibling tools it supersedes for this data (bfactor_view, putty_view), so an agent can distinguish it from the crowded view/color tool family without inspecting schemas.

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?

Explicitly routes the agent: 'which is why bfactor_view and putty_view get these models backwards... Use this instead for predicted models.' The condition that selects this tool (AlphaFold/predicted models) is stated along with the alternatives it replaces. The inverse case (experimental structures where B-factor is real) is only implied, not spelled out, so it falls short of a full 5.

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