langchain-box-mcp-adapter

by box-community

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About

This sample implements the Langchain MCP adapter to the Box MCP server.

Details

Author
box-community
GitHub stars
1
Downloads
331
Categories
AI

- Langchain integration using ChatOpenAI model.
- Connects to Box MCP server via stdio transport.
- Dynamically loads tools from the MCP server.
- Creates a React-style agent for user prompts.
- Provides rich console output with markdown and typewriter effects.

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

  1. Download and install Highlight from highlightai.com/download
  2. Navigate to the plugins tab and select "Add Custom Plugin"
  3. Configure the plugin with the settings below
    Plugin Name langchain-box-mcp-adapter
    Command (node, npx, python, etc.)

    Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.

  4. Enable "Start Automatically" if you want the plugin to start when Highlight launches

From the repository

Clone the repository, install dependencies with uv sync, create a .env file with the required credentials (LangSmith, OpenAI, Box), and ensure the Box MCP server is accessible. Update the StdioServerParameters path in src/simple_client.py or src/graph.py with the correct path to your MCP server. Run the simple client with uv run src/simple_client.py or the graph-based agent via langgraph dev.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "langchain-box-mcp-adapter": {
            "langchain-box-mcp-adapter": {
                "command": "uv",
                "args": [
                    "run",
                    "src/simple_client.py"
                ]
            }
        }
    }
}

McpServers

{
    "langchain-box-mcp-adapter": {
        "command": "uv",
        "args": [
            "run",
            "src/simple_client.py"
        ]
    }
}

langchain-box-mcp-adapter

This sample project implements the Langchain MCP adapter to the Box MCP server. It demonstrates how to integrate Langchain with a Box MCP server using tools and agents.

Features

- Langchain Integration: Utilizes Langchain's ChatOpenAI model for AI interactions.
- MCP Server Communication: Connects to the Box MCP server using stdio transport.
- Tool Loading: Dynamically loads tools from the MCP server.
- Agent Creation: Creates a React-style agent for handling user prompts and tool interactions.
- Rich Console Output: Provides a user-friendly console interface with markdown rendering and typewriter effects.

Requirements

- Python 3.13 or higher
- Dependencies listed in pyproject.toml:
- langchain-mcp-adapters>=0.0.8
- langchain-openai>=0.3.12
- langgraph>=0.3.29
- rich>=14.0.0

Setup

1. Clone the repository:

   git clone <repository-url>
   cd langchain-box-mcp-adapter
2. Install dependencies:

bash
uv sync

3. Create a .env file in the root of the project and fill in the information.
yaml
LANGSMITH_TRACING = "true"
LANGSMITH_API_KEY =
OPENAI_API_KEY =

BOX_CLIENT_ID = ""
BOX_CLIENT_SECRET = ""
BOX_SUBJECT_TYPE = "user"
BOX_SUBJECT_ID = ""


3. Ensure the MCP server is set up and accessible at the specified path in the project.

4. Update the StdioServerParameters in src/simple_client.py or src/graph.py with the correct path to your MCP server script.

python
server_params = StdioServerParameters(
command="uv",
args=[
"--directory",
"/your/absolute/path/to/the/mcp/server/mcp-server-box",
"run",
"src/mcp_server_box.py",
],
)

Usage


Running the Simple Client


To run the simple client:

bash
uv run src/simple_client.py

This will start a console-based application where you can interact with the AI agent. Enter prompts, and the agent will respond using tools and AI capabilities.

Running the Graph-Based Agent (LangGraph)

The graph-based agent can be used by invoking the make_graph function in src/graph.py. This is useful for more complex workflows.
bash uv run langgraph dev --config src/langgraph.json ``` You should see something like: Langgraph studio

Project Structure

- src/simple_client.py: Main entry point for the simple client. - src/graph.py: Contains the graph-based agent setup. - src/console_utils/console_app.py: Utility functions for console interactions. - src/langgraph.json: Configuration for the LangGraph integration.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.
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