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pocket_view

Visualize the binding pocket cavity around a ligand as a colored surface showing hydrophobic, polar, positive, and negative regions, with sidechains and hydrogen bonds.

Instructions

Visualizes the binding pocket cavity around a ligand as a colored surface.

The pocket (all residues within 5 Å of the ligand) is shown as a semi-transparent surface colored by chemical character: orange=hydrophobic, white=polar, skyblue=positive, salmon=negative. Pocket residue sidechains are shown as sticks. The ligand is shown as yellow sticks. H-bonds between the ligand and pocket are drawn as cyan dashes. The protein backbone is shown as a thin grey cartoon for context.

Args: obj_name: PyMOL object name (e.g. "1abc") resn: Ligand residue name (e.g. "ATP", "LIG", "ANP")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
resnYes
obj_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries the full burden. It thoroughly describes the visual output: semi-transparent surface colored by hydrophobicity/charge, sticks for pocket sidechains and ligand, H-bonds as dashes, and protein backbone cartoon. It does not mention side effects or requirements, but the behavior is well-documented.

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 concise and well-structured: a one-sentence summary followed by a bullet-like list detailing the visualization elements. Every sentence adds useful information without redundancy.

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

Completeness4/5

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

The output schema exists, so the description need not explain return values. It covers the visual output in detail, but could mention prerequisites (e.g., object and ligand must be loaded) or that it modifies the PyMOL scene.

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

Parameters4/5

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

The schema has 0% description coverage, so the description compensates by explaining the parameters: obj_name as PyMOL object name and resn as ligand residue name with examples. This adds meaning beyond the schema titles, though more detail on allowed values or formats would improve it.

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

Purpose5/5

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

The description clearly states the tool visualizes the binding pocket cavity around a ligand as a colored surface, with specific details on what is shown and the coloring scheme. It distinguishes itself from siblings like 'ligand_view' or 'hydrophobic_surface_view' by focusing on the pocket cavity and chemical character coloring.

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

Usage Guidelines3/5

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

The description explains what the tool does but does not provide explicit guidance on when to use it versus alternatives like 'ligand_view' or 'interface_view'. No context is given for when the tool is appropriate or when not to use it.

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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