How to Use the ComboCurve MCP in LangChain

Chain your production forecasts directly into LangChain workflows without leaving the terminal.

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Connect ComboCurve MCP to LangChain

Create your Vinkius account to connect ComboCurve 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 ComboCurve with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build automated reasoning pipelines

Connect your agents to live data using `get_project` and `list_forecasts` to drive decision logic. Your LangChain agent evaluates the output of these calls to determine if it should pull more data or conclude the task. This MCP server provides the raw inputs for your chains. You can pass the response from `get_monthly_volumes` directly into subsequent nodes for processing or analysis.

Connect wells to custom chains

Use `get_well` or `search_wells_by_name` to feed specific asset data into your memory buffers. Your agent now holds the exact well metadata required for complex downstream calculations. Everything happens within your defined graph structure. The agent executes these calls as nodes, maintaining full observability through your existing tracing stack.

Manage operational scenarios programmatically

Fetch current project setups with `list_scenarios` to inform your agent about existing constraints. You gain a direct line to your data without writing manual API wrappers. `get_scenario_details` provides the granular context needed for your agent to verify assumptions. It operates as a stateless link in your broader automation architecture.

Setup guide

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

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Common questions about ComboCurve MCP in LangChain

You instantiate the server and pass the tool list into your agent's constructor. This allows the model to invoke ComboCurve functions as part of its standard reasoning loop.

Yes, you can aggregate multiple server instances within your client configuration. This lets your agent switch contexts between different project IDs during a single session.

Your data stays behind the connection endpoint you define. The server only transmits well and forecast records that your specific agent requests during execution.

The server uses standard transport protocols compatible with asynchronous execution. Your LangChain event loop manages these requests alongside your other integrated tools.

Since you are using the framework, you can hook the tool inputs and outputs into your tracing provider. This logs exactly what the agent sent to the server and what it received back.

Use it with your favorite AI tools

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