To make tool inputs model-friendly, focus on structuring data consistently, normalizing values, and ensuring clarity. Models rely on predictable input formats to function correctly, so use standardized schemas like JSON or CSV with well-defined fields. For example, if a model expects user data, structure inputs as {"name": "John", "age": 30} rather than free-form text. Normalize numerical data to a consistent scale (e.g., 0-1 or z-scores) to prevent features with larger ranges from dominating predictions. Categorical data should be encoded consistently—like one-hot encoding for limited categories or embedding layers for text. Avoid ambiguity: specify units (e.g., "weight_kg": 75) and use unambiguous labels (e.g., "is_active": true/false instead of "status": “Y/N”).
Next, prioritize reducing noise and handling missing data. Models perform poorly with irrelevant or inconsistent inputs, so exclude features that don’t contribute to predictions. For instance, if building a spam filter, include email content and sender domain but omit timestamps if they’re irrelevant. Address missing values by either removing incomplete records, imputing defaults (e.g., median for numbers, “unknown” for categories), or adding flags like "missing_age": true. Validate inputs against expected ranges or patterns—e.g., reject negative values for age or invalid email formats. Tools like JSON Schema can enforce rules, such as requiring "temperature" to be a float between -50 and 100. This prevents errors during inference and ensures the model receives clean data.
Finally, document input requirements thoroughly and test edge cases. Provide explicit examples of valid inputs, such as a sample API payload, and list supported data types (e.g., "price" must be a float). If the model requires specific preprocessing steps—like lowercasing text or resizing images to 224x224 pixels—document these and offer utility functions to automate them. For instance, share a Python script that converts raw sensor data into the expected format. Test inputs with diverse scenarios, including empty fields, extreme values, or unexpected characters, to ensure robustness. By making input expectations clear and automating validation, you reduce integration errors and help developers use the tool effectively.