Response from ADK Answering Agent (experimental, answer may be inaccurate)

TLDR: You can use an after_tool_callback to intercept the result of the read_file tool and prevent its content from being sent to the LLM. This allows the tool to run without incurring the cost or truncation issues of passing large amounts of data to the model.

Hello! That's a great question. You can achieve this by using callbacks, which allow you to hook into the execution of tools and modify their behavior.

For your specific use case, the after_tool_callback is the most suitable solution. It runs after a tool has executed but before its output is sent to the LLM. You can inspect the result and decide what to send back to the model.

Here is an example of how you can implement this:

from adk.core import Message
from adk.llms import LLM
from adk.mcp import MultiConversationalProcess
from adk.tools import Tool

def after_tool_callback(
    tool: Tool, args: dict, result: dict | str
) -> dict | str:
    """
    This callback is executed after a tool runs.
    If the tool is 'read_file', it replaces the file content with a summary message.
    """
    if tool.name == "read_file":
        # The tool has run, but we intercept the result.
        # Instead of sending the file content to the LLM,
        # we send this dictionary.
        return {
            "status": "success",
            "message": "File has been read, but content is not displayed.",
            "filename": args.get("path"),  # Assuming 'path' is an arg to read_file
        }
    # For any other tool, return the result as is.
    return result

# When creating your MCP, add the callback:
my_mcp = MultiConversationalProcess(
    llm=my_llm,
    tools=[read_file_tool, write_file_tool],
    after_tool_callback=after_tool_callback,
)

In this example:

1. The after_tool_callback function checks if the tool that just ran is named read_file.
2. If it is, instead of returning the actual result (which contains the large file content), it returns a new dictionary. This dictionary informs the LLM that the operation was successful without including the file's data.
3. If any other tool runs, its result is passed through to the LLM without modification.

This approach ensures that the read_file tool is executed as intended by the agent, but you maintain control over what information is passed back to the LLM, thus avoiding potential high costs and content truncation.

You can find more detailed information about callbacks and their capabilities in the official documentation.

[1] ADK Callbacks Documentation
[2] Design Patterns and Best Practices for Callbacks
[3] Agent Team Tutorial (mentions callbacks)