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What is a JSON schema for tool arguments?

A JSON schema for tool arguments is a machine-readable description of what inputs a tool expects, including the names, types, and constraints of its parameters.

Why it matters

When an LLM calls a tool, it needs a reliable way to format the arguments so the tool can actually run. A JSON schema helps by telling the model and the application:

• which fields are required
• what type each field should be
• whether values must match a pattern, range, or enum
• how to validate the request before executing the tool

In practice, teams use this to reduce malformed tool calls, make tool use more predictable, and catch errors early. It is especially useful when an agent can choose among many tools or when a single tool has several parameters.

How it works

A tool definition usually includes a name, a description, and an input schema written in JSON Schema. The schema describes the shape of the argument object the tool expects.

At runtime, the model produces arguments that should match that schema. For example, if a tool takes city and units, the schema can say city is a string, units is one of a fixed set of strings, and city is required.

The application can then validate the generated arguments against the schema before calling the tool. If the arguments do not match, the app can reject them, ask the model to try again, or repair them depending on its workflow.

A small but important point: “JSON schema” here usually means a JSON Schema-style contract for the tool’s input, not a general schema for all data in the system.

Tiny concrete example

Tool: get_weather

Schema-like input shape:

{
  "type": "object",
  "properties": {
    "city": { "type": "string" },
    "units": { "type": "string", "enum": ["metric", "imperial"] }
  },
  "required": ["city"],
  "additionalProperties": false
}

Valid tool arguments:

{
  "city": "Paris",
  "units": "metric"
}

Invalid tool arguments:

{
  "city": 42,
  "units": "kelvin"
}

Common pitfalls / when NOT to use it

• Too weak a schema: If you only describe types loosely, the model may still produce arguments that are technically valid JSON but not valid for your tool.
• Too strict a schema: Overly rigid constraints can make tool calling brittle, especially if the model needs to infer missing details.
• Confusing schema with business logic: JSON Schema can validate structure and some constraints, but it should not replace application-side rules.
• Using it when the tool is free-form: If a tool accepts arbitrary text or an unstructured payload, a schema may add little value.
• Assuming it guarantees correctness: A schema validates format, not intent. A well-formed request can still be the wrong one.

If you need the model to choose values from a closed set or fill in a structured request, a JSON schema is usually the right starting point. If the input is highly unstructured, plain text may be simpler.

Related terms

• What is function calling?
• What is the Model Context Protocol (MCP)?
• What is an MCP server?
• What is a tool schema / tool definition?
• What is parallel tool calling?
• What is tool result / tool message?
• What is a computer-use / browser-use tool?
• What is a code execution tool (sandbox)?

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