The AI Interview - Master AI/ML Interviews

Validate and normalize tool parameters against a schema:

Check all required params are present
Validate types match (str, int, float, bool)
Apply default values for missing optional params
Return normalized params with defaults applied

Type Conversion:

Try to convert values to correct type
'123' → 123 for int type
'true' → True for bool type

Error Format: {'param': 'name', 'error': 'description'}

Examples

Example 1:

Input: validate_tool_input({'name': 'test'}, {'name': {'type': 'str', 'required': True}})

Output: {'valid': True, 'errors': [], 'normalized_params': {'name': 'test'}}

Explanation: Required param present, correct type

Example 2:

Input: validate_tool_input({}, {'count': {'type': 'int', 'required': False, 'default': 5}})

Output: {'valid': True, 'errors': [], 'normalized_params': {'count': 5}}

Explanation: Missing optional param, default applied

Starter Code

def validate_tool_input(params, schema):
    """
    Validate tool parameters against a JSON-like schema.
    
    Schema format:
    {
        'param_name': {'type': 'str|int|float|bool', 'required': True/False, 'default': val},
        ...
    }
    
    Returns:
        dict with 'valid' (bool), 'errors' (list), 'normalized_params' (dict)
    """
    # Your implementation here
    pass

Tool Input Validator

Examples

Starter Code