Steve Nyemba
3 hours ago
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# Transport Agent Notebook
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This notebook provides a way to interact with the **data-transport MCP server** using LangChain and Marimo.
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## Overview
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The `notebook.py` file implements a `TransportAgent` class that bridges the gap between Large Language Models (LLMs) and the capabilities of the data-transport system via the **Model Context Protocol (MCP)**. It also demonstrates how to perform direct data access using the `transport` library.
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## How it Works
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The `notebook.py` file demonstrates how to integrate MCP with LangChain through the following steps:
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1. **Dynamic Tool Discovery**: Instead of hard-coding tool definitions, the `TransportAgent` fetches the OpenAPI specification from the MCP server (at `http://localhost:8090/openapi.json`).
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2. **Schema Mapping**: It automatically parses the OpenAPI `components` and `paths` to generate LangChain `StructuredTool` instances. This ensures that the agent always has the latest tools available as defined by the MCP server.
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3. **Agent Construction**: These tools are then passed into a LangChain agent. The agent uses these tools to fulfill user requests, such as listing databases or performing data analysis.
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4. **Interactive Interface**: The Marimo framework is used to provide a clean, interactive UI where users can type questions and see the agent's thought process and results.
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## Code Breakdown
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### `TransportAgent` Class
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The heart of the notebook. It manages:
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- **LLM Configuration**: Supports `ollama`, `openai`, and `azure_openai` backends.
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- **Tool Initialization (`_init_mcp_tools`)**:
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- Fetches the `openapi.json` from the local MCP server.
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- Maps API types (string, integer, etc.) to Python types.
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- Dynamically creates `StructuredTool` objects with proper descriptions and argument schemas.
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- **Agent Initialization (`_init_agent`)**: Combines the chosen LLM with the discovered tools into a functional agent.
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- **Query Handling (`get`)**: Takes a natural language prompt and returns the final response from the agent's execution chain.
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## How to leverage MCP with LangChain (Summary)
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To integrate MCP with LangChain in this project:
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1. **Expose via OpenAPI**: The MCP server must provide an `openapi.json` endpoint.
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2. **Automate Tool Creation**: Use a helper function (like `_init_mcp_tools` in this notebook) to iterate over the OpenAPI paths and generate `StructuredTool` objects.
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3. **Bind to Agent**: Pass the list of generated tools to a LangChain agent (e.g., `create_agent`).
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4. **Dynamic Updates**: Because the tools are loaded at runtime from the OpenAPI spec, any update to the MCP server's capabilities is automatically reflected in the agent.
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## Usage
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To run this notebook, ensure the MCP server is running on `localhost:8090` and then execute the marimo notebook:
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```bash
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marimo edit www/html/_plugins/notebook.py
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```
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