Agent Tools

Understanding how tools extend agent capabilities

Tools give your agents superpowers. Without tools, agents can only chat. With tools, they can search, calculate, and interact with the world.

Diagram

Quick Start

python

from praisonaiagents import Agent

# Define a tool (any Python function)
def calculate(expression: str) -> str:
    """Calculate a math expression"""
    return str(eval(expression))

# Give the tool to your agent
agent = Agent(
    instructions="You help with math problems",
    tools=[calculate]
)

agent.start("What is 25 * 48?")

How Tools Work

Diagram

Creating Tools

Write a Python Function

python

def get_weather(city: str) -> str:
    """Get the weather for a city"""
    # Your logic here
    return f"Weather in {city}: Sunny, 72°F"

Add Type Hints

python

# Type hints help the agent understand inputs/outputs
def search_web(query: str, max_results: int = 5) -> str:
    """Search the web for information"""
    return f"Found {max_results} results for: {query}"

Add to Agent

python

agent = Agent(
    instructions="You help users find information",
    tools=[get_weather, search_web]
)

Built-in Tools

PraisonAI includes ready-to-use tools:

python

from praisonaiagents import Agent

# Web search (requires API key)
agent = Agent(
    instructions="Search and summarize",
    tools=["tavily"]  # or "duckduckgo", "you", "exa"
)

# Enable web search simply
agent = Agent(
    instructions="Research topics",
    web=True  # Built-in web search
)

Web Search

tools=["tavily"] or web=True

File Operations

Read, write, and manage files

Code Execution

Run Python code safely

MCP Tools

Connect to external services

Complete Example

python

from praisonaiagents import Agent

# Tool 1: Calculator
def calculate(expression: str) -> str:
    """Calculate a math expression"""
    try:
        return str(eval(expression))
    except:
        return "Error: Invalid expression"

# Tool 2: Unit converter
def convert_units(value: float, from_unit: str, to_unit: str) -> str:
    """Convert between units"""
    conversions = {
        ("km", "miles"): 0.621371,
        ("miles", "km"): 1.60934,
        ("kg", "lbs"): 2.20462,
        ("lbs", "kg"): 0.453592,
    }
    factor = conversions.get((from_unit, to_unit), 1)
    result = value * factor
    return f"{value} {from_unit} = {result:.2f} {to_unit}"

# Agent with multiple tools
agent = Agent(
    instructions="You help with math and unit conversions",
    tools=[calculate, convert_units]
)

agent.start("Convert 100 km to miles, then calculate 62 * 3")

Tool Best Practices

Add Docstrings

Describe what the tool does so the agent knows when to use it

Use Type Hints

def tool(x: str) -> str: helps the agent understand inputs

Handle Errors

Return helpful error messages instead of crashing

One Purpose

Each tool should do one thing well

MCP Tools (Advanced)

Connect to external services using Model Context Protocol:

python

from praisonaiagents import Agent, MCP

agent = Agent(
    instructions="Search the web",
    tools=MCP(
        command="npx",
        args=["-y", "@anthropic/mcp-server-brave-search"],
        env={"BRAVE_API_KEY": "your-key"}
    )
)

Next: Agent Memory

Learn how agents remember conversations.