How to Use the InnoVint MCP in LangChain

Q: How do I pass InnoVint tools to a LangChain agent?

Use the langchain-mcp-adapters package to convert the MCP tools. Initialize MultiServerMCPClient with the Vinkius endpoint, call client.gettools(), and pass them directly to your createagent call.

Q: How does LangChain handle multi-facility routing in InnoVint?

Your agent uses a ReAct loop to query listwineries first, then branches out to check specific vessels. The output of one tool dynamically determines the parameters for the next call in the chain.

Q: How does Vinkius protect my proprietary wine chemistry data?

All chemistry logs retrieved via listanalyses run inside a zero-trust, ephemeral V8 Isolate Sandbox. Your connection token is never exposed to the LLM, and your data is wiped the instant the tool call finishes.

Run multi-step winemaking operations by linking InnoVint data tools directly into your LangChain MCP workflows.

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Setup InnoVint with LangChain

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Chain cellar histories into chemical analysis pipelines

You run `list_actions` to pull the complete treatment history of a Pinot Noir lot and feed those results directly into the next link in your LangChain sequence. Your agent looks at the physical adjustments made by cellar hands, then decides whether to call `list_analyses` to check the sulfur dioxide levels. This multi-step execution lets LangChain handle complex winery reasoning without human intervention between steps. You watch the entire process unfold inside LangSmith, tracking every tool input, output, and token cost in real time.

Automating additive calculations via LangChain MCP Server tools

Calling `list_additives` gives your LangChain agent the exact regulatory limits and standard dosages allowed for your current batch. The agent compares these regulatory thresholds against the active wine chemistry retrieved from `get_lot` to calculate precise additions. Because LangChain supports over 500 integrations, you can instantly push these calculated values to external databases or cellar log sheets. The MCP server runs the math, verifies the limits, and writes the final cellar instruction in a single pass.

Map vessel distribution across multiple facilities

You query `list_vessels` to inspect tank capacities, fill levels, and current locations across your entire footprint. Your LangChain agent processes this physical layout to determine where to transfer incoming juice during harvest. Combining this with `list_wineries` allows the agent to coordinate space planning across separate physical facilities. You build a routing pipeline that keeps your cellar team moving without scheduling bottlenecks.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by InnoVint. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about InnoVint MCP in LangChain

Use the `langchain-mcp-adapters` package to convert the MCP tools. Initialize `MultiServerMCPClient` with the Vinkius endpoint, call `client.get_tools()`, and pass them directly to your `create_agent` call.

Yes, every tool execution is fully visible in LangSmith. You see the exact parameters sent to `get_lot` or `list_analyses` alongside latency and token metrics.

Your agent uses a ReAct loop to query `list_wineries` first, then branches out to check specific vessels. The output of one tool dynamically determines the parameters for the next call in the chain.

LangChain clients are stateless by default to keep execution fast. If you need to maintain context across multiple cellar steps, use `client.session()` to lock in a persistent session.

All chemistry logs retrieved via `list_analyses` run inside a zero-trust, ephemeral V8 Isolate Sandbox. Your connection token is never exposed to the LLM, and your data is wiped the instant the tool call finishes.

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