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batch_molecule_lookup

Convert multiple molecule names to SMILES strings in batch for workflows or screening, with optional error skipping.

Instructions

Convert multiple molecule names to SMILES in batch.

Args: molecule_names: List of molecule names to convert to SMILES skip_failures: Skip molecules that fail lookup instead of stopping

Useful for preparing multiple molecules for workflows or screening.

Returns: Dictionary mapping input names to SMILES strings (or error messages)

Examples: # Drug screening set result = batch_molecule_lookup([ "aspirin", "ibuprofen", "paracetamol", "caffeine" ]) # Returns: { # "aspirin": "CC(=O)Oc1ccccc1C(=O)O", # "ibuprofen": "CC(C)Cc1ccc(C(C)C(=O)O)cc1", # "paracetamol": "CC(=O)Nc1ccc(O)cc1", # "caffeine": "CN1C=NC2=C1C(=O)N(C(=O)N2C)C" # }

# Mixed input types
result = batch_molecule_lookup([
    "benzene",           # Common name
    "50-78-2",          # CAS number
    "ethanoic acid"     # IUPAC name
])

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
molecule_namesYesList of molecule names to convert to SMILES
skip_failuresNoSkip molecules that fail lookup instead of stopping

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 provided, the description carries the full burden and does well by disclosing key behaviors: it explains the batch conversion process, the 'skip_failures' parameter's effect on error handling, and the return format (dictionary mapping names to SMILES or errors). However, it omits details like rate limits, authentication needs, or performance characteristics.

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, returns, examples) and uses bullet points in examples for readability. However, the 'Args' section redundantly repeats schema information, and the examples are lengthy, slightly reducing efficiency. Overall, it is front-loaded and informative.

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?

Given the tool's moderate complexity, 100% schema coverage, and the presence of an output schema (implied by 'Returns' section), the description is complete. It covers purpose, parameters, return values, and provides practical examples, leaving no significant gaps for an AI agent to understand and invoke the tool correctly.

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 the schema already fully documents both parameters. The description repeats the parameter descriptions verbatim in the 'Args' section, adding no additional semantic context beyond what the schema provides. This meets the baseline but does not enhance understanding.

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 specific action ('Convert multiple molecule names to SMILES in batch') and distinguishes it from the sibling 'molecule_lookup' tool by emphasizing batch processing. The title 'batch_molecule_lookup' reinforces this distinction, making the purpose immediately apparent.

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 provides clear context for when to use this tool ('Useful for preparing multiple molecules for workflows or screening'), but it does not explicitly state when not to use it or mention alternatives like the sibling 'molecule_lookup' for single-molecule conversions. The guidance is helpful but lacks explicit exclusions.

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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