How to Use the Moving Average Engine MCP in LlamaIndex

Q: How do I connect the Moving Average Engine to LlamaIndex?

Install llama-index-tools-mcp. Set up a BasicMCPClient, wrap it in McpToolSpec, and call totoollistasync() to pass the functions to your agent.

Q: Can I filter which technical indicators the agent uses?

You can use the allowedtools filter to restrict the agent to specific functions. If you only want it running SMA calculations, you configure the tool spec accordingly.

Index exact financial indicators into your LlamaIndex vector stores without math hallucinations.

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Connect Moving Average Engine MCP to LlamaIndex

Create your Vinkius account to connect Moving Average 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 Moving Average Engine with LlamaIndex

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Ground RAG Apps with Exact Math

The `calculate_moving_average` tool generates deterministic Simple (SMA) and Exponential (EMA) moving averages for LlamaIndex applications. Language models cannot reliably compute recursive formulas like EMA, making this external engine mandatory for financial data. You send raw pricing arrays into the MCP Server, and it returns the exact mathematical values. Your agent then indexes these precise trend indicators directly into your vector store for future querying.

Make API Data Searchable in LlamaIndex

Querying past market sessions requires accurate technical indicators. When your FunctionAgent runs a moving average calculation, LlamaIndex treats the output as ground truth and embeds that specific trend line into your knowledge base. A user can ask about the 50-day SMA from last week, and the index returns the exact figure computed by the tool. You eliminate the risk of the model guessing a number based on similar text in its training data.

Combine Live Trends with Documents

Financial analysis requires mixing real-time math with static reports. This MCP integration allows your agent to get historical price data, calculate the exact EMA, and compare it against indexed earnings transcripts. The tool output becomes just another highly reliable document in your semantic search pipeline. You get RAG applications that actually understand directional market trends based on hard math.

Setup guide

Set up Moving Average 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 Moving Average 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 Moving Average Engine tools.",
)
response = await agent.run("List recent Moving Average Engine data")

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Common questions about Moving Average Engine MCP in LlamaIndex

Install `llama-index-tools-mcp`. Set up a `BasicMCPClient`, wrap it in `McpToolSpec`, and call `to_tool_list_async()` to pass the functions to your agent.

Storing the exact output prevents the model from attempting to recalculate complex formulas during future queries. You get a permanent, searchable record of the mathematical truth.

You can use the `allowed_tools` filter to restrict the agent to specific functions. If you only want it running SMA calculations, you configure the tool spec accordingly.

The agent reads the JSON schema for the calculation tool and knows exactly how to format the numerical array and period parameters to get a valid response.

The engine processes your arrays in a zero-trust environment. Every moving average calculation runs in a temporary container that handles the math and immediately shuts down without logging the input values.

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