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validate_smiles

Validate SMILES strings and extract molecular properties using RDKit to ensure chemical structure accuracy for computational chemistry workflows.

Instructions

Validate a SMILES string and return basic molecular properties.

Args: smiles: SMILES string to validate

Uses RDKit to validate SMILES and extract basic properties.

Returns: Dictionary with validation status and properties if valid

Examples: result = validate_smiles("CC(=O)O") # Returns: { # "valid": True, # "canonical_smiles": "CC(=O)O", # "molecular_formula": "C2H4O2", # "molecular_weight": 60.05 # }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
smilesYesSMILES string to validate
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses the tool uses RDKit and returns a dictionary with validation status and properties, which is helpful. However, it doesn't mention error handling, performance characteristics, rate limits, or authentication requirements, leaving behavioral gaps.

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 appropriately sized and front-loaded with the core purpose. However, the structure includes redundant sections like Args (which repeats schema info) and Examples (which is helpful but could be more concise). Most sentences earn their place, but minor trimming is possible.

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

Completeness3/5

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

Given the tool's moderate complexity (validation with property extraction), no annotations, and no output schema, the description is partially complete. It covers the basic operation and return structure but lacks details on error cases, performance, or integration with sibling tools, leaving room for improvement.

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 documents the single parameter. The description adds minimal value beyond the schema by repeating 'SMILES string to validate' in the Args section. No additional syntax, format details, or constraints are provided beyond what's in 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 description clearly states the tool's purpose with specific verbs ('validate a SMILES string' and 'return basic molecular properties'), identifies the resource (SMILES string), and distinguishes it from sibling tools like molecule_lookup or batch_molecule_lookup by focusing on validation rather than lookup operations.

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 context through the examples and mentions RDKit, but doesn't explicitly state when to use this tool versus alternatives like molecule_lookup or when not to use it. No explicit guidance on prerequisites or exclusions is provided.

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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