How to Use the Cedar AI MCP in LangChain

Build multi-step rail logistics chains in LangChain with Cedar AI to automate complex work order processing.

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Connect Cedar AI MCP to LangChain

Create your Vinkius account to connect Cedar AI to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Cedar AI with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain Railcar Operations

Connect `list_work_orders` to `get_work_order_details` to build autonomous reasoning agents. Your agent parses work order requirements and triggers specific rail movements automatically. This MCP Server provides the granular tool outputs needed for LangSmith tracing. You see exactly how the agent decides to execute `pickup_cars` or `setout_cars` based on real-time inventory data.

Automate Waybill Workflows

Feed `list_waybills` directly into your LangChain logic to track freight documentation. You avoid manual data entry by letting the agent extract waybill data for downstream processing. Your chains handle state transitions by calling `update_work_order` once the logic satisfies your defined business constraints. This creates a tight loop between your database and the physical rail yard.

Manage Railcar Status

Trigger `update_railcar_status` from your agent to mark assets as 'Bad Order' or 'Clean' immediately upon inspection. The tool calls act as concrete nodes in your LangGraph pipeline. You gain full control over the rail lifecycle by chaining these tools together. The agent handles the decision logic while the server executes the write operations against the rail system.

Setup guide

Set up Cedar AI MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Cedar AI tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "cedar-ai-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Cedar AI transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Cedar AI MCP in LangChain

Install the MCP adapters and point your client to the server endpoint. You pass the tool list directly into your agent constructor to enable function calling.

Yes. The client supports aggregating multiple servers so your agent can query rail data alongside your existing internal databases.

It provides the raw input and output data for every tool call. You can pipe these directly into LangSmith to debug your reasoning chains.

It is stateless by default. You use the client session feature to maintain persistent context between your agent's turns.

Your railcar inventory and waybill data travel over encrypted connections. We use ephemeral sessions so no sensitive records persist on the server once your connection closes.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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