Single tool 'generate_smiles' has a complete input schema with 14 well-described parameters (all with type and description), but the tool description is minimal (67 chars) and lacks critical context about when/why to use it, what it returns, or error handling. The tool name lacks a clear action verb (should be 'generate_smiles_string' or similar for clarity). No output schema is documented, LLMs cannot predict the return type or structure. No error handling guidance provided. Parameter descriptions are adequate but lack constraints (e.g., valid ranges for numeric fields). The Pydantic model is well-structured internally, but this is not exposed to the MCP schema.

• Expand tool description to 100-200 chars: 'Generate a SMILES string for a drug-like molecule matching specified chemical constraints (H-bond donors/acceptors, molecular weight, LogP, Lipinski compliance, etc.). Returns a valid SMILES string or an error if constraints are unsatisfiable. Use this to design molecules with specific pharmacological properties.'
• Add explicit output schema documentation: 'Returns: string (SMILES notation). Example: "CC(C)Cc1ccc(cc1)C(C)C(O)=O". If generation fails, returns error with constraint violation details.'
• Add min/max constraints to numeric parameters in the schema: max_hbond_donors (0-20), max_hbond_acceptors (0-20), max_molecular_weight (50-1000), max_clogp (-5 to 10), exact_hbond_donors (0-20), exact_hbond_acceptors (0-20).
• Implement error handling that returns structured errors: '{"error": "unsatisfiable_constraints", "message": "No valid SMILES found matching: max_hbond_donors=0, presence_of_macrocycle=true. Try relaxing constraints.", "suggestion": "Increase max_hbond_donors or remove macrocycle requirement."}'
• Rename tool to 'generate_smiles_string' for clarity, or add a suffix to distinguish from potential future tools like 'generate_smiles_batch' or 'validate_smiles'.