Requests stock status
AI Models & Assistants / Knowledge library
Tool Schema
Defines a tool's name, purpose, input fields, types, and validation rules
Why learn Tool Schema?
It improves tool selection and allows arguments to be checked before application code runs.
The problem it solves
It replaces brittle free-form action parsing with an explicit typed interface.
Where it came from
A tool schema translates an application capability into a machine-readable contract the model can call. Clear descriptions and constrained fields reduce ambiguity, but schema validation does not establish authorization or make side effects safe.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Define Tool Schema and identify its place in an agent architecture.
- Choose a task where Tool Schema improves an agent system.
- Diagnose one failure caused by misusing Tool Schema.
- Interpret a trace or state transition involving Tool Schema.
- Explain one operational tradeoff of Tool Schema.
Documentation and sources
- OpenAI function calling guide (platform.openai.com)
Connect the ideas
Explore next
Real World Example
Logistics Inventory Query Automation
Logistics companies managing distributed warehouse inventory.
The business problem
Manual lookup of stock levels across multiple regional databases creates significant latency in order fulfillment workflows.
How this topic solves it
Implementing a Tool Schema allows the model to directly query internal inventory APIs, automating data retrieval without human intervention.
- Define a JSON schema for the inventory lookup function including required parameters like SKU and warehouse_id.
- Register the tool definition within the API request payload to expose the capability to the model.
- Implement the application-side handler to execute the database query when the model generates a tool call.
- Return the structured tool call output back to the model to synthesize the final natural language response.
The sequence of interactions between the user, model, and inventory system.
Generates tool call based on schema
Executes SQL/API query
Returns stock count
- User → prompt → Model
- Model → tool_call → App Logic
- App Logic → query → Inventory DB
- Inventory DB → data → App Logic
- App Logic → tool_call_output → Model
Expected business outcome
Reduced fulfillment latency and improved accuracy in stock availability reporting.
Limitations and checks
The model may hallucinate arguments if the JSON schema lacks strict validation rules; developers must check the schema definition for 'additionalProperties: false' to enforce strict parameter adherence.