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

Index your market analysis results into LlamaIndex using the MACD & RSI Oscillator Engine.

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Connect MACD & RSI Oscillator Engine MCP to LlamaIndex

Create your Vinkius account to connect MACD & RSI Oscillator Engine to LlamaIndex 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 LlamaIndex

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Perplexity

Vector-ready indicator output

The `calculate_macd_rsi` tool generates values that you can immediately feed into your LlamaIndex knowledge base. You aren't just reading numbers; you are creating queryable market context. Your RAG application can now retrieve past MACD or RSI states alongside your documents. This makes it possible to ask questions about historical market regimes based on your own calculated data.

Unified knowledge indexing

LlamaIndex handles the MCP tool output as structured knowledge. By including these indicators in your index, you bridge the gap between live market data and static documentation. Set up your indexing pipeline to store these results systematically. This creates a searchable history of indicator signals tied to the specific price data you provided.

Grounded decision making

Eliminate hallucinations by forcing your agent to rely on the `calculate_macd_rsi` output stored in your index. You get verifiable data points rather than guesses. When your agent queries the index, it pulls from the exact calculation results you previously generated. This ensures that your technical analysis remains grounded in reality.

Setup guide

Set up MACD & RSI Oscillator Engine MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all MACD & RSI Oscillator Engine MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to MACD & RSI Oscillator Engine tools.",
)
response = await agent.run("List recent MACD & RSI Oscillator Engine data")

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Common questions about MACD & RSI Oscillator Engine MCP in LlamaIndex

Use the llama-index-tools-mcp integration to hook the server into your project. Once connected, your tool outputs become part of your searchable vector store.

Yes. By indexing the tool outputs, you create a persistent history that your agents can query to understand market behavior over time.

Yes, it provides the quantitative data points that LlamaIndex needs to build a complete picture of your market data for RAG applications.

The server executes locally, and your LlamaIndex vector store manages the persistence of the resulting data in your own storage backend.

This MCP server uses local-only execution and ephemeral sessions. Your price data is processed in memory and never shared with external indexing services.

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