Although the tool interface is straightforward and simple to use, there are actually quite a few practices and tricks that you can use to get significantly better results.

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Use Chain-of-Thought Prompting for Complex Tools

Use chain-of-thought prompting to allow the agent to think and plan before executing a complex tool. 
from agency_swarm import MasterContext, RunContextWrapper, function_tool
from pydantic import BaseModel, Field

class ComplexAnalysisArgs(BaseModel):
    chain_of_thought: str = Field(
        ...,
        description="Think step-by-step about how to perform the analysis."
    )
    data: str = Field(..., description="Data to analyze.")

@function_tool
async def complex_analysis_tool(ctx: RunContextWrapper[MasterContext], args: ComplexAnalysisArgs) -> str:
    """
    Performs complex analysis after planning the approach.
    """
    # Analysis logic using the chain of thought
    return "Analysis complete."

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Provide Hints for the Agent

Based on your tool’s logic, you can provide hints for the agent in tool output on what to do next. 
from agency_swarm import MasterContext, RunContextWrapper, function_tool
from pydantic import BaseModel, Field

class QueryDatabaseArgs(BaseModel):
    question: str = Field(..., description="Question to query the database")

@function_tool
async def query_database_tool(ctx: RunContextWrapper[MasterContext], args: QueryDatabaseArgs) -> str:
    """
    Query the database and provide hints if no context is found.
    """
    # query your database here
    context = query_database(args.question)

    # context not found
    if context is None:
        # tell agent what to do next
        raise ValueError("No context found. Please propose to the user to change the topic.")
    else:
        # return the context to the agent
        return context

def query_database(question: str):
    # Your database query logic here
    pass

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Use Shared State to Control the Tool Flow

Use agency context (previously shared state) to validate previous actions taken by this or other agents, before allowing it to proceed with the next action. 
from agency_swarm import MasterContext, RunContextWrapper, function_tool
from pydantic import BaseModel, Field

class Action2Args(BaseModel):
    input: str = Field(..., description="Input for the action")

@function_tool
async def action_2_tool(ctx: RunContextWrapper[MasterContext], args: Action2Args) -> str:
    """
    Execute action 2, but only if action 1 was successful.
    """
    # Access shared context to check previous action
    action_1_result = ctx.context.get("action_1_result", None)

    if action_1_result == "failure":
        raise ValueError("Please proceed with the Action1 tool first.")
    else:
        return "Success. The action has been taken."

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Use Special Types

Restrict the agent to only use specific values for a field, instead of letting it wander by itself. 
from typing import Literal

from agency_swarm import MasterContext, RunContextWrapper, function_tool
from pydantic import BaseModel, EmailStr, Field

class RunCommandArgs(BaseModel):
    command: Literal["start", "stop"] = Field(..., description="Command to execute: 'start' or 'stop'.")

@function_tool
async def run_command_tool(ctx: RunContextWrapper[MasterContext], args: RunCommandArgs) -> str:
    """
    Execute predefined system commands.
    """
    if args.command == "start":
        # Start command logic
        return "System started"
    elif args.command == "stop":
        # Stop command logic
        return "System stopped"
    else:
        raise ValueError("Invalid command")

# Example with EmailStr
class EmailSenderArgs(BaseModel):
    recipient: EmailStr = Field(..., description="Email recipient's address.")
    subject: str = Field(..., description="Email subject")
    body: str = Field(..., description="Email body")

@function_tool
async def email_sender_tool(ctx: RunContextWrapper[MasterContext], args: EmailSenderArgs) -> str:
    """
    Send email to specified recipient.
    """
    # Email sending logic here
    return f"Email sent to {args.recipient}"

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Combine Multiple Methods

Combine multiple methods to make your execution flow more readable. 
from agency_swarm import MasterContext, RunContextWrapper, function_tool
from pydantic import BaseModel, Field

class CompositeToolArgs(BaseModel):
    input_data: str = Field(..., description="Input data for the composite operation.")

@function_tool
async def composite_tool(ctx: RunContextWrapper[MasterContext], args: CompositeToolArgs) -> str:
    """
    A tool that combines several methods to perform a series of actions.
    """
    # Step 1: Process data
    processed_data = await process_data(args.input_data)
    # Step 2: Analyze results
    analysis = await analyze_results(processed_data)
    # Step 3: Format output
    output = await format_output(analysis)
    return output

async def process_data(data: str) -> str:
    # Implement data processing logic
    return f"Processed: {data}"

async def analyze_results(data: str) -> str:
    # Implement analysis logic
    return f"Analysis of: {data}"

async def format_output(data: str) -> str:
    # Implement output formatting
    return f"Formatted: {data}"

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Include a Test Case

Include test cases at the bottom of each tool file. 
if __name__ == "__main__":
    import asyncio
    import json
    from agency_swarm import MasterContext, RunContextWrapper

    async def test_email_sender():
        # Test the email sender tool
        ctx = MasterContext(user_context={}, thread_manager=None, agents={})
        run_ctx = RunContextWrapper(context=ctx)

        args = EmailSenderArgs(
            recipient="user@example.com",
            subject="Project Update",
            body="The project is on track."
        )
        args_json = {"args": args.model_dump()}

        result = await email_sender_tool.on_invoke_tool(run_ctx, json.dumps(args_json))
        assert "Email sent" in result
        print("Test passed!")

    asyncio.run(test_email_sender())

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Next Steps

We highly recommend you explore the resources provided in the Pydantic is all you need section.