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morph_states

Validate topology and generate morphs only between states with shared atom correspondence. Use to animate deformation ensembles while refusing ill-posed morphs for independently modelled structures.

Instructions

Interpolate between states, refusing when interpolation is ill-posed.

The value here is the refusal, not the interpolation — PyMOL morphs natively. A morph is only meaningful when every state shares a topology, so that atom i in state 1 is the same atom as atom i in state 2. That holds for a deformation-model ensemble (3DFlex, DynaMight). It does not hold for volumes reconstructed and modelled independently, and a morph across those animates a correspondence nobody established.

Note that cmd.morph is Incentive-only. On open-source PyMOL the topology check still runs and reports; only the interpolation is unavailable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoName for the morph object (defaults to "<obj_name>_morph")
stepsNoNumber of interpolated frames to generate (default 30)
obj_nameYesMulti-state PyMOL object name to interpolate
validate_onlyNoRun the topology check and report, without creating a morph

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations, the description fully carries the behavioral burden. It discloses that the tool refuses ill-posed interpolations, runs a topology check even when interpolation is unavailable, and supports validate-only mode. This is rich, honest behavioral context that goes beyond the schema.

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 with a concise purpose statement, a rationale paragraph, and an important availability note. It is slightly verbose (~100 words) but each sentence contributes meaningful context, earning a 4 rather than a 5.

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

Completeness5/5

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

The description is remarkably complete despite lacking annotations. It explains purpose, valid use cases, limitations, and even open-source behavior. An output schema exists to handle return-value details, so no essential context is missing.

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?

The input schema covers 100% of parameters with descriptions, so the baseline is 3. The description adds high-level context about topology and refusal, which indirectly relates to obj_name and validate_only, but does not explicitly detail parameter usage beyond the schema.

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 opening sentence 'Interpolate between states, refusing when interpolation is ill-posed' clearly states the action and resource. It distinguishes itself from native PyMOL morphing by emphasizing the topology-check refusal, setting it apart from sibling tools like align or intra_fit.

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

Usage Guidelines5/5

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

The description explicitly states when morphing is valid (shared topology, e.g., deformation-model ensembles) and when it is not (independently modelled volumes). It also mentions the Incentive-only limitation and the behavior on open-source PyMOL, providing clear guidance on constraints and expectations.

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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