How to Use the Katana MCP in LangChain

Build multi-step ERP reasoning chains with your Katana MCP Server and LangChain agents.

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Connect Katana MCP to LangChain

Create your Vinkius account to connect Katana 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 Katana with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automate inventory checks in LangChain

Chain your logic by passing output from `list_inventory` directly into subsequent reasoning steps. This lets your agent evaluate stock levels before making a decision. Your agent monitors supply chains by feeding `get_inventory_by_variant` results into your LangSmith traces. You see exactly how the chain reacts to specific SKU counts.

Dynamic sales order creation for LangChain

Trigger `create_sales_order` within a complex workflow once your agent verifies customer data. It removes the need for manual data entry during order processing. Your pipeline handles the full lifecycle by linking `list_customers` with order creation. The agent decides when to execute based on the chain's internal state.

Katana MCP Server data aggregation

Combine Katana data with external databases using standard LangChain connectors. You build agents that pull from `list_materials` while querying local vector stores. This setup allows for cross-referencing live production data against historical records. Your agent interprets the full picture without moving outside the chain.

Setup guide

Set up Katana 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 Katana 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({
    "katana-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 Katana transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Katana MCP in LangChain

Install the necessary adapters and initialize the server using the provided endpoint. Use the MultiServerMCPClient to register your tools and pass them into your agent constructor.

You manage rate limits by implementing custom middleware or logic within your LangChain pipeline. The server itself processes requests as they arrive, so handle your pacing in the agent's execution loop.

Yes, use the client session method to maintain state across multiple turns. This keeps your agent's context alive while it works through complex manufacturing tasks.

Absolutely. Every tool input and output from your Katana tools appears in your LangSmith traces. You monitor the agent's decision-making process in real-time.

Your data stays within your local execution environment or authorized VPC. The MCP server uses a single endpoint token to authenticate, and no raw data is stored by the bridge.

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