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: ```python 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: ```python 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: ```python 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