putty_view

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

Provides detailed behavioral traits: tube radius scaling with B-factor, color mapping, surface transparency, ligand stick representation, and black background. Since no annotations are provided, the description carries full burden and does so effectively.

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?

Concise and well-structured: opening sentence defines purpose, followed by visual details, then argument. No redundant sentences.

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?

Covers input and visual output adequately for a single-parameter tool with output schema. Could mention return value or side effects, but not critical given simplicity.

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?

With 0% schema description coverage, the description adds meaning by specifying the parameter is a PyMOL object name and provides an example ('1abc'). This compensates for the lack of schema documentation.

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?

Clearly states it visualizes protein flexibility using putty representation, a specific verb and resource. Distinguishes from siblings like bfactor_view and cartoon by specifying putty (tube-width) representation and B-factor scaling.

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?

Describes what the tool does but does not explicitly state when to use it versus alternatives like bfactor_view. Usage is implied by the visualization type, but no exclusions or guidance is provided.

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