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submit_multistage_opt_workflow

Submit a multi-stage optimization workflow to refine molecular geometries through sequential calculations with increasing accuracy. Handles complex molecules, transition states, and solvent environments.

Instructions

Submit a multi-stage optimization workflow for sequential geometry refinement.

Args: initial_molecule: SMILES string representing the molecule xtb_preopt: Pre-optimize with xTB before main stages (default: False) transition_state: Optimize to a transition state instead of minimum (default: False) frequencies: Calculate vibrational frequencies after optimization (default: False) solvent: Solvent environment, name or SMILES (default: empty for gas phase) name: Workflow name for identification and tracking folder_uuid: UUID of folder to organize this workflow. Empty string uses default folder. max_credits: Maximum credits to spend on this calculation. 0 for no limit.

Multi-stage optimization uses sequential calculations with increasing accuracy to efficiently find high-quality optimized geometries. This is particularly useful for:

  • Large or complex molecules

  • Transition state searches

  • High-accuracy geometry requirements

  • Systems where initial geometry is poor

Returns: Workflow object representing the submitted workflow

Examples: # Basic multi-stage optimization result = submit_multistage_opt_workflow( initial_molecule="CC(=O)O", name="Acetic acid optimization" )

# High-accuracy optimization with frequencies
result = submit_multistage_opt_workflow(
    initial_molecule="c1ccccc1",
    frequencies=True,
    name="Benzene with frequencies"
)

# Transition state optimization with xTB pre-optimization
result = submit_multistage_opt_workflow(
    initial_molecule="CC=CC",
    transition_state=True,
    xtb_preopt=True,
    name="Butene rotation TS"
)

# Solvated molecule optimization
result = submit_multistage_opt_workflow(
    initial_molecule="CN1C=NC2=C1C(=O)N(C(=O)N2C)C",
    solvent="water",
    frequencies=True,
    name="Caffeine in water"
)

# Fast screening optimization
result = submit_multistage_opt_workflow(
    initial_molecule="CCCCCCCCCC",
    xtb_preopt=True,
    name="Decane quick opt"
)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
initial_moleculeYesSMILES string of the molecule for multi-stage optimization
xtb_preoptNoWhether to pre-optimize with xTB before main optimization stages
transition_stateNoWhether this is a transition state optimization
frequenciesNoWhether to calculate vibrational frequencies after optimization
solventNoSolvent environment for calculations (name or SMILES). Empty string for gas phase
nameNoWorkflow name for identification and trackingMulti-Stage Optimization Workflow
folder_uuidNoUUID of folder to organize this workflow. Empty string uses default folder
max_creditsNoMaximum credits to spend on this calculation. 0 for no limit
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes what the tool does (submits a workflow for sequential geometry refinement), mentions efficiency benefits, and provides context about credit limits. However, it doesn't disclose potential rate limits, authentication requirements, or error conditions.

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 clear sections (purpose, args, use cases, returns, examples). While comprehensive, some sentences could be more concise, such as the multi-paragraph explanation of use cases. However, all content earns its place by providing necessary context.

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?

For a complex 8-parameter workflow tool with no annotations and no output schema, the description provides substantial context: clear purpose, parameter explanations, use cases, return value description, and extensive examples. It adequately compensates for the lack of structured metadata, though could benefit from more behavioral details.

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?

With 100% schema description coverage, the baseline is 3. The description adds value by grouping parameters in an 'Args:' section with clear explanations and default values, and the examples demonstrate how parameters interact in practical scenarios, providing context beyond the schema's technical descriptions.

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's purpose with specific verbs ('submit a multi-stage optimization workflow') and resource ('sequential geometry refinement'). It distinguishes from siblings by focusing on multi-stage optimization for geometry refinement, unlike other workflow tools for docking, descriptors, or protein-related tasks.

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 to use this tool ('particularly useful for: - Large or complex molecules - Transition state searches - High-accuracy geometry requirements - Systems where initial geometry is poor'). It also provides multiple examples showing different use cases, helping distinguish from alternative workflow tools.

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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