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.tools import BaseTool
from pydantic import Field

class ComplexAnalysisTool(BaseTool):
    """
    Performs complex analysis after planning the approach.
    """
    chain_of_thought: str = Field(
        ...,
        description="Think-step-by-step about how to perform the analysis."
    )
    data: str = Field(..., description="Data to analyze.")

    def run(self):
        # Analysis logic
        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. 
class QueryDatabase(BaseTool):
    question: str = Field(...)

    def run(self):
        # query your database here
        context = self.query_database(self.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

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Use Shared State to Control the Tool Flow

Use shared_state to validate previous actions taken by this or other agents, before allowing it to proceed with the next action. 
class Action2(BaseTool):
    input: str = Field(...)

    def run(self):
        if self._shared_state.get("action_1_result", None) is "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

class RunCommand(BaseTool):
    """
    Execute predefined system commands.
    """
    command: Literal["start", "stop"] = Field(..., description="Command to execute: 'start' or 'stop'.")

    def run(self):
        if self.command == "start":
            # Start command logic
            pass
        elif self.command == "stop":
            # Stop command logic
            pass
        else:
            raise ValueError("Invalid command")
or use special Pydantic types like EmailStr. 
from pydantic import EmailStr

class EmailSender(BaseTool):
    recipient: EmailStr = Field(..., description="Email recipient's address.")

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Combine Multiple Methods

Combine multiple methods to make your execution flow more readable. 
class CompositeTool(BaseTool):
    """
    A tool that combines several methods to perform a series of actions.
    """
    input_data: str = Field(..., description="Input data for the composite operation.")

    def run(self):
        # Step 1: Process data
        processed_data = self.process_data(self.input_data)
        # Step 2: Analyze results
        analysis = self.analyze_results(processed_data)
        # Step 3: Format output
        output = self.format_output(analysis)
        return output

    def process_data(self, data):
        # Implement data processing logic
        pass

    def analyze_results(self, data):
        # Implement analysis logic
        pass

    def format_output(self, data):
        # Implement output formatting
        pass

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Include a Test Case

Include test cases at the bottom of each tool file. 
if __name__ == "__main__":
    # Test the EmailSender tool
    email_sender = EmailSender(
        chain_of_thought="Plan to inform the team about the update.",
        recipient="user@example.com",
        subject="Project Update",
        body="The project is on track."
    )
    assert email_sender.run() == "Email sent successfully."

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Next Steps

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