Python: Pre & Post Processing

Prerequisites

This tutorial assumes that you have set up MCP Toolbox with a basic agent as described in the local quickstart.

This guide demonstrates how to implement these patterns in your Toolbox applications.

Implementation

{{< tabpane persist=header >}} {{% tab header="ADK" text=true %}} The following example demonstrates how to use ToolboxToolset with ADK's pre and post processing hooks to implement pre and post processing for tool calls.

{{< include "adk/agent.py" "python">}}

You can also add model-level (before_model_callback, after_model_callback) and agent-level (before_agent_callback, after_agent_callback) hooks to intercept messages at different stages of the execution loop.

For more information, see the ADK Callbacks documentation. {{% /tab %}} {{% tab header="Langchain" text=true %}} The following example demonstrates how to use ToolboxClient with LangChain's middleware to implement pre- and post- processing for tool calls.

{{< include "langchain/agent.py" "python" >}}

You can also add model-level (wrap_model) and agent-level (before_agent, after_agent) hooks to intercept messages at different stages of the execution loop. See the LangChain Middleware documentation for details on these additional hook types. {{% /tab %}} {{< /tabpane >}}

Results

The output should look similar to the following.

{{< notice note >}} The exact responses may vary due to the non-deterministic nature of LLMs and differences between orchestration frameworks. {{< /notice >}}

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