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Tools give AI models the ability to call functions, enabling them to interact with external systems, access real-time data, and perform complex operations beyond text generation.

​
What are Tools?

A tool is a function that an AI model can choose to call during generation. The model:
  1. Analyzes the user’s request
  2. Decides which tool(s) to use
  3. Calls the tool(s) with appropriate arguments
  4. Incorporates the tool results into its response
This enables agentic workflows where the model acts autonomously to solve problems.

​
Defining Tools

import { genkit, z } from 'genkit';
import { googleAI } from '@genkit-ai/google-genai';

const ai = genkit({ plugins: [googleAI()] });

const getWeatherTool = ai.defineTool(
  {
    name: 'getWeather',
    description: 'Get current weather for a city. Use this when users ask about weather conditions.',
    inputSchema: z.object({
      city: z.string().describe('City name, e.g., "Paris" or "New York"'),
    }),
    outputSchema: z.object({
      temperature: z.number(),
      conditions: z.string(),
      humidity: z.number(),
    }),
  },
  async ({ city }) => {
    // Call weather API...
    return {
      temperature: 72,
      conditions: 'Sunny',
      humidity: 65,
    };
  }
);

// Use in generation
const { text } = await ai.generate({
  model: googleAI.model('gemini-2.0-flash'),
  prompt: 'What is the weather in Paris?',
  tools: [getWeatherTool],
});

console.log(text);
// "The weather in Paris is currently sunny with a temperature of 72°F and 65% humidity."

​
How Tool Calling Works

The tool execution flow: 
┌─────────────────────────────────────────────────────────────────────┐
│                      Tool Execution Flow                                │
├─────────────────────────────────────────────────────────────────────┤
│                                                                         │
│  ┌──────────┐      ┌──────────┐      ┌──────────┐      ┌──────────┐    │
│  │  Model   │ ───► │   Tool   │ ───► │ Execute  │ ───► │  Model   │    │
│  │ Request  │      │ Request  │      │ Function │      │ Continue │    │
│  └──────────┘      └──────────┘      └──────────┘      └──────────┘    │
│                          │                                             │
│                          ▼ (if interrupt=True)                         │
│                    ┌──────────┐                                        │
│                    │  Pause   │ ────► User confirms ────► Resume       │
│                    └──────────┘                                        │
└─────────────────────────────────────────────────────────────────────┘
  1. User sends prompt: “What’s the weather in Paris?”
  2. Model analyzes prompt and available tools
  3. Model decides to call getWeather with {city: "Paris"}
  4. Genkit executes the tool function
  5. Tool returns {temperature: 72, conditions: "Sunny", humidity: 65}
  6. Model incorporates result into its response
  7. Model returns: “The weather in Paris is currently sunny…”

​
Multiple Tools

Provide multiple tools for the model to choose from: 
@ai.tool()
def get_weather(city: str) -> dict:
    """Get current weather for a city."""
    return {'temperature': 72, 'conditions': 'Sunny'}

@ai.tool()
def search_restaurants(city: str, cuisine: str) -> list[dict]:
    """Search for restaurants in a city."""
    return [
        {'name': 'Le Bistro', 'rating': 4.5},
        {'name': 'Chez Pierre', 'rating': 4.8},
    ]

@ai.tool()
def book_reservation(restaurant: str, time: str, party_size: int) -> dict:
    """Book a restaurant reservation."""
    return {'confirmation': 'ABC123', 'restaurant': restaurant}

response = await ai.generate(
    prompt='Plan dinner in Paris tonight for 4 people. I like Italian food.',
    tools=['get_weather', 'search_restaurants', 'book_reservation'],
)

print(response.text)
# Model may call multiple tools:
# 1. get_weather("Paris") to check conditions
# 2. search_restaurants("Paris", "Italian") to find options
# 3. book_reservation("Le Bistro", "7:00 PM", 4) to book

​
Tool Choice Strategies

Control how the model uses tools: 
// Auto: Model decides when to use tools (default)
const { text } = await ai.generate({
  prompt: 'What is the weather?',
  tools: [getWeatherTool],
  toolChoice: 'auto',
});

// Required: Model MUST use at least one tool
const { text } = await ai.generate({
  prompt: 'Get me some data',
  tools: [getWeatherTool, searchTool],
  toolChoice: 'required',
});

// None: Model cannot use tools
const { text } = await ai.generate({
  prompt: 'Tell me about weather',
  tools: [getWeatherTool],
  toolChoice: 'none',
});

​
Tool Interrupts (Human-in-the-Loop)

Interrupts let you pause tool execution for human approval - perfect for sensitive operations like financial transactions or data deletion. 
const deleteUserTool = ai.defineTool(
  {
    name: 'deleteUser',
    description: 'Delete a user account (requires confirmation)',
    inputSchema: z.object({ userId: z.string() }),
    interrupt: true, // Pause execution for approval
  },
  async ({ userId }) => {
    // This won't execute automatically
    return `User ${userId} deleted`;
  }
);

// First generate - tool is NOT executed
const response = await ai.generate({
  prompt: 'Delete user account 12345',
  tools: [deleteUserTool],
});

// Check for interrupts
if (response.interrupts && response.interrupts.length > 0) {
  const interrupt = response.interrupts[0];
  console.log('Tool call pending approval:', interrupt.toolRequest.name);
  console.log('Arguments:', interrupt.toolRequest.input);
  
  // Show to user: "Confirm delete user 12345? [Yes/No]"
  const userApproved = await getUserConfirmation();
  
  if (userApproved) {
    // Resume with approval
    const resumedResponse = await ai.generate({
      prompt: 'Delete user account 12345',
      tools: [deleteUserTool],
      messages: response.messages,
      resume: {
        respond: deleteUserTool.respond(interrupt, 'User confirmed deletion'),
      },
    });
  }
}

​
Interrupt Flow

┌─────────────────────────────────────────────────────────────────────┐
│ 1. Model decides to call deleteUser(userId="12345")                  │
├─────────────────────────────────────────────────────────────────────┤
│ 2. Genkit sees interrupt=true, PAUSES execution                      │
├─────────────────────────────────────────────────────────────────────┤
│ 3. Response includes tool call in response.interrupts                │
├─────────────────────────────────────────────────────────────────────┤
│ 4. Your app shows confirmation dialog to user                        │
├─────────────────────────────────────────────────────────────────────┤
│ 5. User approves/rejects                                              │
├─────────────────────────────────────────────────────────────────────┤
│ 6. Resume with tool response (approved or rejected)                  │
├─────────────────────────────────────────────────────────────────────┤
│ 7. Model incorporates response and continues                          │
└─────────────────────────────────────────────────────────────────────┘

​
Max Turns (Limiting Tool Iterations)

Prevent infinite tool calling loops: 
const { text } = await ai.generate({
  prompt: 'Research quantum computing',
  tools: [searchTool, analyzeTool],
  maxTurns: 5, // Stop after 5 model-tool interactions
});
Default is typically 5 turns. Each turn = model call + tool execution(s).

​
Return Tool Requests Without Execution

Inspect what tools the model wants to call without executing them: 
const { text, toolRequests } = await ai.generate({
  prompt: 'What is the weather in Tokyo?',
  tools: [getWeatherTool],
  returnToolRequests: true, // Don't auto-execute
});

console.log(toolRequests);
// [{ name: 'getWeather', input: { city: 'Tokyo' } }]

// Manually execute
const result = await getWeatherTool({ city: 'Tokyo' });
Useful for:
  • Debugging tool selection
  • Custom tool execution logic
  • Rate limiting
  • Logging/auditing

​
Tool Best Practices

​
1. Write Clear Descriptions

The model uses descriptions to decide when to call tools: 
@ai.tool()
def get_weather(city: str) -> dict:
    """Get current weather for a city.
    
    Use this tool when:
    - User asks about weather conditions
    - User mentions temperature, rain, or forecast
    - Planning outdoor activities
    
    Args:
        city: Full city name (e.g., "San Francisco", not "SF")
    
    Returns:
        Weather data including temperature (Fahrenheit), conditions, humidity
    """
    ...

​
2. Use Detailed Schema Descriptions

const searchTool = ai.defineTool(
  {
    name: 'search',
    description: 'Search the web for information',
    inputSchema: z.object({
      query: z.string()
        .describe('Search query. Be specific and include relevant keywords.'),
      maxResults: z.number()
        .optional()
        .describe('Maximum number of results to return (default: 5, max: 20)'),
    }),
  },
  async (input) => { /* ... */ }
);

​
3. Handle Errors Gracefully

Tools should return user-friendly error messages: 
@ai.tool()
def get_stock_price(symbol: str) -> dict:
    """Get current stock price."""
    try:
        # API call...
        return {'symbol': symbol, 'price': 150.25}
    except APIError as e:
        # Return error as data, not exception
        return {'error': f'Could not fetch price for {symbol}: {str(e)}'}

​
4. Use Interrupts for Sensitive Actions

Always use interrupt=True for:
  • Financial transactions
  • Data deletion
  • Sending emails/messages
  • Modifying user data
  • External API calls with side effects

​
5. Keep Tools Focused

Each tool should do one thing well: 
# Good: Focused tools
@ai.tool()
def get_weather(city: str) -> dict: ...

@ai.tool()
def get_forecast(city: str, days: int) -> dict: ...

# Bad: Tool does too much
@ai.tool()
def weather_everything(city: str, action: str, days: int) -> dict: ...

​
Real-World Example: Travel Agent

from genkit import Genkit
from genkit.plugins.google_genai import GoogleGenAI, gemini_2_0_flash

ai = Genkit(plugins=[GoogleGenAI()], model=gemini_2_0_flash)

@ai.tool()
def get_weather(city: str) -> dict:
    """Get current weather for a city."""
    # Call weather API
    return {'temperature': 72, 'conditions': 'Sunny'}

@ai.tool()
def search_flights(origin: str, destination: str, date: str) -> list[dict]:
    """Search for flights between cities."""
    # Call flight API
    return [
        {'airline': 'United', 'price': 350, 'departure': '10:00 AM'},
        {'airline': 'Delta', 'price': 380, 'departure': '2:00 PM'},
    ]

@ai.tool()
def search_hotels(city: str, checkin: str, checkout: str) -> list[dict]:
    """Search for hotels in a city."""
    # Call hotel API
    return [
        {'name': 'Grand Hotel', 'price': 200, 'rating': 4.5},
        {'name': 'City Inn', 'price': 150, 'rating': 4.0},
    ]

@ai.tool(interrupt=True)
def book_flight(flight_id: str, passenger: dict) -> dict:
    """Book a flight (requires confirmation)."""
    # This will pause for user confirmation
    return {'confirmation': 'ABC123', 'flight': flight_id}

@ai.flow()
async def travel_planner(request: str) -> str:
    """Plan a trip with weather, flights, and hotels."""
    response = await ai.generate(
        prompt=f"Help plan this trip: {request}",
        tools=['get_weather', 'search_flights', 'search_hotels', 'book_flight'],
    )
    
    # Check for interrupts
    if response.interrupts:
        # Return to user for confirmation
        return f"Pending confirmation: {response.interrupts[0].tool_request.input}"
    
    return response.text

# Usage
result = await travel_planner(
    "I want to go to Paris next Friday for 3 days. Book the cheapest flight."
)
# Model will:
# 1. get_weather("Paris") - check conditions
# 2. search_flights("San Francisco", "Paris", "2024-03-15") - find options
# 3. search_hotels("Paris", "2024-03-15", "2024-03-18") - find hotels
# 4. book_flight(...) - PAUSES for user confirmation

​
Next Steps

  • Learn about Flows - building multi-step workflows with tools
  • Explore Prompts - using tools in prompt templates
  • See Observability - tracing tool executions

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