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hydrophobic_surface_view

Color the molecular surface of a PyMOL object by amino acid hydrophobicity, with distinct colors for hydrophobic, polar, positively charged, and negatively charged residues.

Instructions

Colors the molecular surface by amino acid hydrophobicity.

Orange = hydrophobic (ILE, VAL, LEU, PHE, MET, ALA, TRP, PRO), white = polar (SER, THR, CYS, TYR, ASN, GLN, GLY), sky blue = positively charged (ARG, LYS, HIS), salmon = negatively charged (ASP, GLU). A white cartoon is shown beneath a semi-transparent surface. Organic ligands shown as sticks with yellow carbons.

Args: obj_name: PyMOL object name (e.g. "1abc")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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 full burden. It details the visual result: the surface colors, a white cartoon beneath, and organic ligands as sticks with yellow carbons. It does not mention any destructive actions or mutability, but as a view function, this is acceptable.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured: purpose statement, color mapping in bullet-like format, and parameter explanation. The color list is somewhat lengthy but necessary for clarity. It is front-loaded with the main purpose.

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?

Given the tool's simplicity (one parameter) and the presence of an output schema (not shown), the description covers the essential aspects. It explains the visual output and parameter meaning. Missing are prerequisites (e.g., object must exist) and potential error states, but the description is otherwise complete.

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 input schema has 0% description coverage, so the description must compensate. It explains obj_name as 'PyMOL object name' and provides an example ('1abc'), adding meaningful context beyond the schema. Additional details like the requirement that the object must exist could improve, but it is adequate.

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 that the tool colors the molecular surface by amino acid hydrophobicity, and lists the specific color mapping for each amino acid category. This distinguishes it from sibling view tools like electrostatic_view or conservation_view.

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

Usage Guidelines4/5

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

The description implies that the tool should be used to visualize hydrophobicity, but does not explicitly state when not to use it or suggest alternatives. However, the specificity of the color scheme and grouping of amino acids provides clear context.

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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