Creating Tools

Add Python functions as tools that agents can execute. There are two ways: pass functions directly (simple) or use the Tool class (advanced).

Quick Start - Simple Function

The easiest way is to pass a function directly to add_tool():

from lyzr import Studio

studio = Studio(api_key="your-api-key")

# Define functions with type hints and docstrings
def get_weather(city: str) -> str:
    """Get current weather for a city"""
    return f"Weather in {city}: Sunny, 72°F"

def search_products(query: str, max_results: int = 10) -> list:
    """Search products in the catalog"""
    # Your implementation
    return [{"name": "Product A", "price": 29.99}]

def send_email(to: str, subject: str, body: str) -> str:
    """Send an email to a recipient"""
    # Your implementation
    return "Email sent successfully"

# Create agent
agent = studio.create_agent(
    name="Assistant",
    provider="gpt-4o",
    role="Helpful assistant",
    goal="Help users with tasks"
)

# Add functions directly - ADK infers everything from type hints
agent.add_tool(get_weather)
agent.add_tool(search_products)
agent.add_tool(send_email)

# Run
response = agent.run("What's the weather in Paris?")

The ADK automatically:

Uses the function name as the tool name
Uses the docstring as the description
Infers parameters from type hints
Determines required vs optional from default values

Quick Start - Tool Class

For more control, use the Tool class:

from lyzr import Studio
from lyzr.tools import Tool

studio = Studio(api_key="your-api-key")

def search_products(query: str, category: str = "all") -> list:
    """Search products in catalog"""
    return [{"name": "Product A", "price": 29.99}]

# Create tool with explicit schema
search_tool = Tool(
    name="search_products",
    description="Search for products in the catalog by query and optional category",
    parameters={
        "type": "object",
        "properties": {
            "query": {
                "type": "string",
                "description": "Search query"
            },
            "category": {
                "type": "string",
                "description": "Product category filter",
                "enum": ["all", "electronics", "clothing", "home"]
            }
        },
        "required": ["query"]
    },
    function=search_products
)

agent = studio.create_agent(
    name="Shopping Assistant",
    provider="gpt-4o",
    role="E-commerce assistant",
    goal="Help users find products"
)
agent.add_tool(search_tool)

response = agent.run("Find me wireless headphones")

When to Use Each Approach

Approach	Best For
Simple Function	Most use cases, rapid development
Tool Class	Custom descriptions, enum values, complex schemas

Simple Function Requirements

For the ADK to auto-infer tool parameters:

def my_function(
    required_param: str,           # Required (no default)
    optional_param: int = 10       # Optional (has default)
) -> str:                          # Return type
    """Short description of what the function does"""  # Becomes tool description
    return "result"

Supported Types

Python Type	Inferred JSON Type
`str`	`"string"`
`int`	`"integer"`
`float`	`"number"`
`bool`	`"boolean"`
`list`	`"array"`
`dict`	`"object"`

Tool Class

from lyzr.tools import Tool

tool = Tool(
    name: str,                    # Required: Unique identifier
    description: str,             # Required: What the tool does (for LLM)
    parameters: Dict[str, Any],   # Required: JSON Schema for inputs
    function: Callable = None     # Optional: Python function to execute
)

Parameters

Parameter	Type	Required	Description
`name`	str	Yes	Unique tool name (no spaces)
`description`	str	Yes	Clear description for LLM
`parameters`	dict	Yes	JSON Schema defining inputs
`function`	Callable	No	Python function to execute

Defining Parameters

Parameters use JSON Schema format:

Basic Schema

parameters = {
    "type": "object",
    "properties": {
        "param_name": {
            "type": "string",        # Type of parameter
            "description": "What this parameter does"
        }
    },
    "required": ["param_name"]       # List of required parameters
}

Supported Types

# String
{"type": "string", "description": "User's name"}

# Integer
{"type": "integer", "description": "Number of items"}

# Number (float)
{"type": "number", "description": "Price in dollars"}

# Boolean
{"type": "boolean", "description": "Include shipping"}

# Array
{"type": "array", "items": {"type": "string"}, "description": "List of tags"}

# Enum (restricted values)
{"type": "string", "enum": ["low", "medium", "high"], "description": "Priority level"}

Complete Example

parameters = {
    "type": "object",
    "properties": {
        "destination": {
            "type": "string",
            "description": "Destination city"
        },
        "departure_date": {
            "type": "string",
            "description": "Departure date (YYYY-MM-DD)"
        },
        "passengers": {
            "type": "integer",
            "description": "Number of passengers"
        },
        "class": {
            "type": "string",
            "enum": ["economy", "business", "first"],
            "description": "Cabin class"
        },
        "direct_only": {
            "type": "boolean",
            "description": "Only show direct flights"
        }
    },
    "required": ["destination", "departure_date"]
}

Auto-Infer Parameters

Generate parameters automatically from function signatures:

from lyzr.tools.local import infer_parameters_from_function

def book_appointment(
    patient_name: str,
    doctor_id: str,
    date: str,
    time: str,
    notes: str = ""
) -> dict:
    """Book a medical appointment"""
    pass

# Auto-generate parameters
params = infer_parameters_from_function(book_appointment)

# Create tool with inferred parameters
tool = Tool(
    name="book_appointment",
    description="Book a medical appointment for a patient",
    parameters=params,
    function=book_appointment
)

Type Mapping

Python Type	JSON Schema Type
`str`	`"string"`
`int`	`"integer"`
`float`	`"number"`
`bool`	`"boolean"`
`list`	`"array"`
`dict`	`"object"`
`Optional[T]`	Type of `T`

Sync and Async Functions

Tools support both synchronous and asynchronous functions:

Synchronous

def get_stock_price(symbol: str) -> float:
    """Get current stock price"""
    import requests
    response = requests.get(f"https://api.stocks.com/{symbol}")
    return response.json()["price"]

stock_tool = Tool(
    name="get_stock_price",
    description="Get current price for a stock symbol",
    parameters={
        "type": "object",
        "properties": {
            "symbol": {"type": "string", "description": "Stock symbol (e.g., AAPL)"}
        },
        "required": ["symbol"]
    },
    function=get_stock_price
)

Asynchronous

async def fetch_user_data(user_id: str) -> dict:
    """Fetch user data from API"""
    import aiohttp
    async with aiohttp.ClientSession() as session:
        async with session.get(f"https://api.example.com/users/{user_id}") as resp:
            return await resp.json()

user_tool = Tool(
    name="fetch_user_data",
    description="Fetch user profile data",
    parameters={
        "type": "object",
        "properties": {
            "user_id": {"type": "string", "description": "User ID"}
        },
        "required": ["user_id"]
    },
    function=fetch_user_data
)

Adding Tools to Agents

Single Tool

agent = studio.create_agent(name="Bot", provider="gpt-4o")
agent.add_tool(my_tool)

Multiple Tools

from lyzr.tools import ToolRegistry

# Create registry
registry = ToolRegistry()
registry.add(search_tool)
registry.add(order_tool)
registry.add(track_tool)

# Add all to agent
for tool in registry.list():
    agent.add_tool(tool)

At Creation Time

agent = studio.create_agent(
    name="Assistant",
    provider="gpt-4o",
    role="Helper",
    tools=[search_tool, order_tool]  # Pass tools directly
)

Tool Registry

Manage collections of tools:

from lyzr.tools import ToolRegistry

registry = ToolRegistry()

# Add tools
registry.add(tool1)
registry.register(tool2)  # Alias for add()

# Get tool by name
tool = registry.get("tool_name")

# List all tools
all_tools = registry.list()

# Check if tool exists
if "my_tool" in registry:
    print("Tool found")

# Remove tool
registry.remove("old_tool")

# Clear all tools
registry.clear()

# Get count
print(len(registry))  # Number of tools

Tool API Format

Convert tools for API registration:

# Single tool
api_format = tool.to_api_format()
# {
#     "name": "get_weather",
#     "description": "Get current weather",
#     "parameters": {...}
# }

# All tools in registry
all_tools_api = registry.to_api_format()
# List of tool API formats

Examples

Database Tool

import sqlite3

def query_customers(name: str = None, email: str = None) -> list:
    """Query customer database"""
    conn = sqlite3.connect("customers.db")
    cursor = conn.cursor()

    query = "SELECT * FROM customers WHERE 1=1"
    params = []

    if name:
        query += " AND name LIKE ?"
        params.append(f"%{name}%")
    if email:
        query += " AND email LIKE ?"
        params.append(f"%{email}%")

    cursor.execute(query, params)
    results = cursor.fetchall()
    conn.close()

    return [{"id": r[0], "name": r[1], "email": r[2]} for r in results]

customer_tool = Tool(
    name="query_customers",
    description="Search for customers by name or email",
    parameters={
        "type": "object",
        "properties": {
            "name": {"type": "string", "description": "Customer name (partial match)"},
            "email": {"type": "string", "description": "Customer email (partial match)"}
        },
        "required": []
    },
    function=query_customers
)

HTTP API Tool

import requests

def create_ticket(title: str, description: str, priority: str = "medium") -> dict:
    """Create a support ticket"""
    response = requests.post(
        "https://api.support.com/tickets",
        json={
            "title": title,
            "description": description,
            "priority": priority
        },
        headers={"Authorization": "Bearer token"}
    )
    return response.json()

ticket_tool = Tool(
    name="create_ticket",
    description="Create a new support ticket",
    parameters={
        "type": "object",
        "properties": {
            "title": {"type": "string", "description": "Ticket title"},
            "description": {"type": "string", "description": "Detailed description"},
            "priority": {
                "type": "string",
                "enum": ["low", "medium", "high", "urgent"],
                "description": "Priority level"
            }
        },
        "required": ["title", "description"]
    },
    function=create_ticket
)

File System Tool

import os

def list_files(directory: str, extension: str = None) -> list:
    """List files in a directory"""
    files = os.listdir(directory)

    if extension:
        files = [f for f in files if f.endswith(extension)]

    return [{"name": f, "path": os.path.join(directory, f)} for f in files]

file_tool = Tool(
    name="list_files",
    description="List files in a directory with optional extension filter",
    parameters={
        "type": "object",
        "properties": {
            "directory": {"type": "string", "description": "Directory path"},
            "extension": {"type": "string", "description": "File extension filter (e.g., '.pdf')"}
        },
        "required": ["directory"]
    },
    function=list_files
)

Best Practices

Clear Descriptions

# Good: Specific and actionable
description="Search the product catalog by name, category, or price range"

# Bad: Vague
description="Search products"

Detailed Parameter Descriptions

# Good
{
    "date": {
        "type": "string",
        "description": "Date in YYYY-MM-DD format (e.g., 2024-03-15)"
    }
}

# Bad
{
    "date": {
        "type": "string",
        "description": "Date"
    }
}

Return Structured Data

# Good: Return dict/list for structured results
def get_user(user_id: str) -> dict:
    return {"id": user_id, "name": "John", "email": "john@example.com"}

# Avoid: Plain strings for structured data
def get_user(user_id: str) -> str:
    return "User John with email john@example.com"

Getting Started

Agents

Knowledge Bases

Memory

Tools

Contexts

RAI Guardrails

Structured Outputs

File & Image Output

Streaming

Reference

​Quick Start - Simple Function

​Quick Start - Tool Class

​When to Use Each Approach

​Simple Function Requirements

​Supported Types

​Tool Class

​Parameters

​Defining Parameters

​Basic Schema

​Supported Types

​Complete Example

​Auto-Infer Parameters

​Type Mapping

​Sync and Async Functions

​Synchronous

​Asynchronous

​Adding Tools to Agents

​Single Tool

​Multiple Tools

​At Creation Time

​Tool Registry

​Tool API Format

​Examples

​Database Tool

​HTTP API Tool

​File System Tool

​Best Practices

​Clear Descriptions

​Detailed Parameter Descriptions

​Return Structured Data

Quick Start - Simple Function

Quick Start - Tool Class

When to Use Each Approach

Simple Function Requirements

Supported Types

Tool Class

Parameters

Defining Parameters

Basic Schema

Supported Types

Complete Example

Auto-Infer Parameters

Type Mapping

Sync and Async Functions

Synchronous

Asynchronous

Adding Tools to Agents

Single Tool

Multiple Tools

At Creation Time

Tool Registry

Tool API Format

Examples

Database Tool

HTTP API Tool

File System Tool

Best Practices

Clear Descriptions

Detailed Parameter Descriptions

Return Structured Data