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Tool calling lets the model request named functions with JSON arguments; your code runs them and returns results in the conversation. SUPA accepts the same tool schemas as OpenAI Chat Completions.

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OpenAI SDK

Use tools on chat.completions.create. Define each tool with type: 'function' and a JSON Schema in function.parameters. 
import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: process.env.SUPA_API_KEY,
  baseURL: 'https://api.supa.works/openai',
});

const response = await openai.chat.completions.create({
  model: 'google/gemma-3-27b-it',
  messages: [
    {
      role: 'user',
      content: 'What is the weather in Boston?',
    },
  ],
  tools: [
    {
      type: 'function',
      function: {
        name: 'get_weather',
        description: 'Get the current weather for a city',
        parameters: {
          type: 'object',
          properties: {
            city: { type: 'string', description: 'City name' },
            unit: {
              type: 'string',
              enum: ['celsius', 'fahrenheit'],
              description: 'Temperature unit',
            },
          },
          required: ['city'],
        },
      },
    },
  ],
  tool_choice: 'auto',
});

const message = response.choices[0]?.message;
console.log(message);
If the model issues a tool call, message.tool_calls contains the function name and arguments string. Parse the arguments, run your function, and send a follow-up request with role: 'tool' messages (or continue the loop until the model responds with text only).

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Vercel AI SDK

Use generateText with tool and Zod inputSchema. Reuse the same SUPA provider as in the Vercel AI SDK guide. 
import { createOpenAI } from '@ai-sdk/openai';
import { generateText, tool } from 'ai';
import { z } from 'zod';

const supa = createOpenAI({
  apiKey: process.env.SUPA_API_KEY,
  baseURL: 'https://api.supa.works/openai/v1',
});

const { text, toolCalls, toolResults } = await generateText({
  model: supa('google/gemma-3-27b-it'),
  prompt: 'What is the weather in Boston?',
  tools: {
    get_weather: tool({
      description: 'Get the current weather for a city',
      inputSchema: z.object({
        city: z.string(),
        unit: z.enum(['celsius', 'fahrenheit']).optional(),
      }),
      execute: async ({ city, unit }) => {
        return {
          city,
          unit: unit ?? 'fahrenheit',
          temperature: 72,
          summary: 'Sunny',
        };
      },
    }),
  },
});

console.log(text);
console.log(toolCalls, toolResults);

​
TanStack AI

Define tools with toolDefinition and .server() for the implementation. Use the same adapter configuration as in the TanStack AI guide. 
import { chat, toolDefinition } from '@tanstack/ai';
import { createOpenaiChat } from '@tanstack/ai-openai';
import type { OpenAIChatModel } from '@tanstack/ai-openai';
import { z } from 'zod';

const adapter = createOpenaiChat(
  'google/gemma-3-27b-it' as OpenAIChatModel,
  process.env.SUPA_API_KEY!,
  { baseURL: 'https://api.supa.works/openai/v1' },
);

const getWeather = toolDefinition({
  name: 'get_weather',
  description: 'Get the current weather for a city',
  inputSchema: z.object({
    city: z.string(),
    unit: z.enum(['celsius', 'fahrenheit']).optional(),
  }),
  outputSchema: z.object({
    city: z.string(),
    temperature: z.number(),
    summary: z.string(),
  }),
}).server(async ({ city, unit }) => ({
  city,
  temperature: 72,
  summary: 'Sunny',
}));

await chat({
  adapter,
  stream: false,
  messages: [
    {
      role: 'user',
      content: [{ type: 'text', content: 'What is the weather in Boston?' }],
    },
  ],
  tools: [getWeather],
});