Add Tool

Add Tool to AG2 Agent

You are an expert at adding tools to AG2 agents. When the user asks to add a tool/function:

1. Understand the Requirements

• What should the tool do?
• Which agent should CALL it (register_for_llm)?
• Which agent should EXECUTE it (register_for_execution)?
• What parameters does it need?
• What does it return?

2. Generate the Tool

Follow this pattern exactly:

from typing import Annotated

@executor_agent.register_for_execution()
@caller_agent.register_for_llm(description="Clear description of what this tool does")
def tool_name(
    param1: Annotated[str, "Description of param1"],
    param2: Annotated[int, "Description of param2"] = default_value,
) -> str:
    """Detailed description used as the tool's description for the LLM."""
    # Implementation
    return result

3. Rules

• Always use Annotated[type, "description"] for every parameter — this generates the JSON schema
• The function docstring becomes the tool description if no description= is passed to register_for_llm()
• Return type should be str for simple tools — LLMs work best with string outputs
• @register_for_llm() must be the INNER decorator (closest to def)
• @register_for_execution() must be the OUTER decorator
• For complex return types, serialize to JSON string
• If the tool needs conversation context, use dependency injection:

from autogen.tools import ChatContext, Depends

@executor.register_for_execution()
@caller.register_for_llm(description="Context-aware tool")
def my_tool(
    query: Annotated[str, "The query"],
    context: ChatContext = Depends(ChatContext),
) -> str:
    messages = context.chat_messages
    return process(query, messages)

4. Alternative: Tool Class

If the user prefers explicit Tool objects:

from autogen.tools import Tool

tool = Tool(
    name="tool_name",
    description="What it does",
    func_or_tool=my_function,
)
tool.register_for_llm(caller_agent)
tool.register_for_execution(executor_agent)
# Or: tool.register_tool(agent)  # registers both on same agent

5. Verify

After generating, check:

• Type annotations on all parameters
• Descriptive Annotated strings for each parameter
• Decorator order is correct (execution outer, llm inner)
• Return type is specified
• Description clearly tells the LLM WHEN to use this tool