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title, type, weight, description
| title | type | weight | description |
|---|---|---|---|
| Python | docs | 1 | How to add pre- and post- processing to your Agents using Python. |
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
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{{% tab header="ADK" text=true %}}
Coming soon.
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The following example demonstrates how to use ToolboxClient with LangChain's middleware to implement pre- and post- processing for tool calls.
{{< include "python/langchain/agent.py" >}}
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.
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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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