json200 B

Tool Function JSON Schema

JSON Schema for a sample weather tool's parameters.

Tool calling / function calling Schema / OpenAPI testing

Preview, first 15 linesjson

{
  "name": "get_weather",
  "parameters": {
    "type": "object",
    "properties": {
      "city": {
        "type": "string"
      }
    },
    "required": [
      "city"
    ]
  }
}

Specifications

Function: get_weather

Testing contract

Expected to pass

Scenario: Exercise Tool Function JSON Schema in its nlp workflow. JSON Schema for a sample weather tool's parameters.
Expected result: top-level keys are name, parameters. Declared feature checks: function=get_weather.

What is a .json file?

JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.

How to use this file

Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.

How to use this file for testing

“Tool Function JSON Schema” is a deterministic Novus Examples fixture for Tool calling / function calling, Schema / OpenAPI testing. JSONL traces of function/tool calls with arguments and results, for testing agent harnesses and tool routers.

Documented properties for this file: JSON · 200 bytes. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.

Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.

AI/ML fixtures are fully synthetic with documented schemas, no real people or data. Test data loaders, tokenizers, annotation converters, embedding/vector stores, or eval-metric parsers against the known structure and fixed seeds.

These JSON fixtures are synthetic SAMPLE auth or tool-call shapes for harnesses, never production secrets or live tokens. Validate schema fields and alg variants against the documented role.

Code examples

import json

with open("tool-json-schema.json") as f:
    data = json.load(f)
print(type(data), len(data))

Generated by generation/ai_wave_f.py. Free for any use, no attribution required, license.