How to Use the MACD & RSI Oscillator Engine MCP in LangChain

Build quantitative reasoning chains in LangChain using precise MACD & RSI Oscillator Engine calculations.

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Connect MACD & RSI Oscillator Engine MCP to LangChain

Create your Vinkius account to connect MACD & RSI Oscillator Engine 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 MACD & RSI Oscillator Engine with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain-ready technical indicators

The `calculate_macd_rsi` tool returns clean, structured data arrays that plug directly into your LangChain agent pipelines. You get raw indicator values ready for immediate downstream processing without parsing messy text. Your agent decides when to trigger these calculations based on the sequence of your chain. This MCP server acts as a deterministic math engine within your broader LangSmith-traced workflows.

Direct tool integration for agents

Integrating this MCP server into your agent requires only the standard adapter setup. Once connected, your agent treats the MACD and RSI math as a native function call. Because this is an MCP server, LangChain manages the tool schema automatically. Your agents get clear, typed inputs and outputs for every market data point they process.

Latency-optimized execution

Calculations happen in 4.2ms locally, keeping your LangChain chains fast. You avoid the network lag of cloud-based trading APIs when you run this server on your own node. Keep your execution logic tight. By minimizing the time spent waiting for indicator math, you give your agents more room to perform complex reasoning before the trade window closes.

Setup guide

Set up MACD & RSI Oscillator Engine 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 MACD & RSI Oscillator Engine 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({
    "macd-rsi-oscillator-engine-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 MACD & RSI Oscillator Engine transactions"
    })
    print(result["messages"][-1].content)

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Common questions about MACD & RSI Oscillator Engine MCP in LangChain

Use the langchain-mcp-adapters package to bridge the connection. Define your transport, initialize the client, and pass the tool list into your agent constructor.

Yes. It acts as a standard tool that your agent can invoke at any step of a chain. The tool output flows directly into the next link in your logic.

Absolutely. Since these are standard MCP tool calls, LangSmith captures every input, output, and latency metric for your indicator calculations.

You pass a standard JSON array of price points to the tool. It returns the calculated indicator values immediately.

This server runs locally or in your private infrastructure. No price data ever leaves your environment, ensuring your proprietary strategy remains private.

Use it with your favorite AI tools

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