MCP Toolbox for Databases

--- title: "Python" type: docs weight: 1 description: > 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](../../getting-started/local_quickstart.md). This guide demonstrates how to implement these patterns in your Toolbox applications. ## Implementation {{< tabpane persist=header >}} {{% tab header="ADK" text=true %}} Coming soon. {{% /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. ```py {{< 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](https://docs.langchain.com/oss/python/langchain/middleware/custom#wrap-style-hooks) 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 >}} ``` AI: Booking Confirmed! You earned 500 Loyalty Points with this stay. AI: Error: Maximum stay duration is 14 days. ```