• Create functions that might be useful to an LLM. Math is a good example because LLMs are bad at math.
• Do an LLM Call and let gemini know about the available tools

import { GoogleGenAI, type FunctionDeclaration } from '@google/genai';

function calculateSum(a, b) {
    return `Sum is ${a + b}`;
}

const ai = new GoogleGenAI({
    apiKey: 'API_KEY',
});

const sumFunctionCall: FunctionDeclaration = {
    name: 'sum',
    description: 'This tool is used to find the sum of 2 numbers',
    parametersJsonSchema: {
      type: 'object',
      properties:{
        a: {
          type:'number',
        },
        b: {
          type:'number',
        },
      },
      required: ['a', 'b'],
    },
};

const response = await ai.models.generateContent({
    model: 'gemini-2.0-flash',
    contents: 'Whats the sum of 2 and 3',
	config: {
		tools: [{functionDeclarations: [sumFunctionCall]}]
	}
});

if (!response) {
	process.exit()
}

const functionCalls = response.functionCalls;

if (functionCalls && functionCalls.length > 0) {
	console.log(`Processing ${functionCalls.length} function calls:`);
	
	const functionResponses = [];
	
	for (const call of functionCalls) {
	    console.log(`Executing: ${call.name} with args:`, call.args);
	    
	    let functionResult;
	    switch (call.name) {
	         case 'sum':
              functionResult = calculateSum(call?.args?.a, call?.args?.b);
              break;
          default:
              functionResult = 'Unknown function';
	    }
	
	    functionResponses.push({
				functionResponse: {
					name: call.name,
					response: { result: functionResult }
				}
	    });
	}

	const finalResult = await ai.models.generateContent({
		model: 'gemini-2.0-flash',
		contents: [
			{
				role: "user",
				parts: [{text: 'Whats the sum of 2 and 3'}]
			},
			response.candidates[0].content,
			{ role: 'user', parts: functionResponses }
		],
	});
	console.log(finalResult.text)
} else {
	console.log('Direct response:', response.text);
}