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chill_plus

Classify water molecules as liquid, ice, or hydrate with CHILL+ to analyze phase composition, class counts, fractions, and O-O cutoff networks in MD simulations.

Instructions

Classify water molecules as liquid, ice, or hydrate with CHILL+.

The result contains the six class counts, per-water classes, fractions, and the unique O-O cutoff network in water input order.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
boxNoPeriodic box in nm (3/6/9 values); else from get_symmetry.
stateNoOne-based PyMOL state to analyze.
cutoff_nmNoCHILL+ O-O cutoff in nm.
selectionNoSelection of the water system.all
object_nameNoObject whose box to use. Defaults to the selection's object.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description carries the burden of disclosing behavior. It details the output components (six class counts, per-water classes, fractions, and O-O cutoff network) and specifies ordering ('in water input order'). This gives a clear picture of what the tool returns, though it does not explicitly state whether it mutates state or requires specific session conditions. The output description is strong enough to earn a 4.

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 exactly two sentences, front-loaded with the primary action. The second sentence lists result components without fluff. Every word contributes to understanding, and there is no redundant or verbose phrasing.

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 tool has an output schema, so return values are already documented there. The description provides the higher-level context: it classifies water phases and highlights the unique O-O network. It does not explain prerequisites like needing a water-only selection or a valid box, but those are covered by parameter defaults and the schema. For a scientific tool of this complexity, the description is sufficiently complete.

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

Parameters3/5

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

Schema description coverage is 100%, so all five parameters already have descriptions. The tool description adds no parameter-level guidance beyond the schema. Per the baseline rule, when schema coverage exceeds 80% and the description adds little, a score of 3 is appropriate.

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 opens with a specific verb and resource: 'Classify water molecules as liquid, ice, or hydrate with CHILL+.' This clearly distinguishes it from sibling analysis tools like order_parameter or hbond_network, which target different properties. It also names the CHILL+ method, giving precise scientific context.

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 implies usage for water phase classification but does not explicitly state when to prefer this over alternatives like identify_cages or hbond_network. No exclusions or comparative guidance are provided, leaving the agent to infer from context. Some guidance on when CHILL+ is appropriate would improve this dimension.

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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