How to Use the MACD & RSI Oscillator Engine MCP in OpenAI Agents SDK

Q: How do I install the MACD & RSI Oscillator Engine MCP server with OpenAI Agents SDK?

Run pip install openai-agents. Create your connection using MCPServerStreamableHttp and pass it to the Agent constructor in your Python script.

Q: How do I cache tools when using OpenAI Agents SDK?

Set cacheToolsList=True during your agent initialization. This speeds up discovery for the MCP server tools.

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Connect MACD & RSI Oscillator Engine MCP to OpenAI Agents SDK

Create your Vinkius account to connect MACD & RSI Oscillator Engine to OpenAI Agents SDK 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 OpenAI Agents SDK

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Quantitative momentum analysis via MCP Server

The `calculate_macd_rsi` tool processes raw price arrays to generate exact technical oscillator values. You pass in your time-series data and configuration parameters, and the engine handles the math locally. OpenAI Agents SDK auto-discovers this endpoint during initialization. You get immediate access to momentum metrics without writing manual indicator logic in Python.

Enforce strict execution guardrails

When your agent calls the `calculate_macd_rsi` tool, built-in guardrails validate the inputs before execution. Algorithmic trading requires absolute precision, and bad data costs money. The MCP protocol isolates the math from the execution logic. Your agent handles the data handoffs safely while the OpenAI dashboard traces every calculation step.

Multi-agent financial architectures

The `calculate_macd_rsi` tool integrates perfectly into multi-agent setups. One agent fetches market data while another runs the math to evaluate momentum shifts. You set `cacheToolsList=True` for performance and let the agents pass the oscillator outputs back and forth. This setup keeps your quantitative analysis modular.

Setup guide

Set up MACD & RSI Oscillator Engine MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all MACD & RSI Oscillator Engine tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives MACD & RSI Oscillator Engine tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate MACD & RSI Oscillator Engine tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="MACD & RSI Oscillator Engine Agent",
            instructions="You have access to MACD & RSI Oscillator Engine tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about MACD & RSI Oscillator Engine MCP in OpenAI Agents SDK

Run `pip install openai-agents`. Create your connection using `MCPServerStreamableHttp` and pass it to the Agent constructor in your Python script.

Yes. You configure one agent to run the oscillator calculations and hand the resulting signals off to an execution agent.

Set `cacheToolsList=True` during your agent initialization. This speeds up discovery for the MCP server tools.

Yes. The Python implementation uses an async context manager to handle the HTTP streamable connection without blocking your main thread.

It reads raw price arrays. The MCP protocol keeps these numerical arrays local to the server, meaning your proprietary market data never leaks into the broader LLM training sets.

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