How to Use the Basis Calculator MCP in LangChain

Chain your commodity data directly in LangChain agents to automate basis risk decisions with precision.

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Connect Basis Calculator MCP to LangChain

Create your Vinkius account to connect Basis Calculator to LangChain — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Basis Calculator with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Chain basis math into workflows

Feed the output of `calculate_current_basis` directly into your agent logic. You get immediate parity data without manual input. Your chains handle the math while your agent focuses on the next step in the pipeline.

Contextualize historical trends

Inject `get_historical_averages` into your reasoning loop to anchor your agent's decisions in regional reality. This MCP gives your agent the hard data it needs to identify price anomalies before they impact your margins.

Predict cycle movements

Use `get_seasonality_trend` as a node in your graph to forecast expected market shifts. Your agents now act on cyclical patterns, ensuring your hedging strategy stays ahead of the agricultural calendar.

Setup guide

Set up Basis Calculator 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 Basis Calculator 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({
    "basis-calculator-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 Basis Calculator transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Basis Calculator MCP in LangChain

You instantiate the client and pass the tool list directly into your agent constructor. This allows your LangChain agent to invoke the tools as part of its standard reasoning chain.

Yes, by using the client session context. This keeps your basis calculations persistent across turns, ensuring the agent remembers previous commodity values.

Absolutely. Since the output of these tools is clean JSON, you can pipe the result of a basis check into any other LangChain integration, like a database or email tool.

Your commodity data stays local to your execution environment. The MCP acts as a bridge, meaning sensitive pricing information never leaves your controlled infrastructure.

You wrap your tool calls in standard try-catch blocks within your chain. This lets you define fallback logic if the pricing data is unavailable.

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