How to Use the Moving Average Engine MCP in Mastra AI

Q: How do I register the Moving Average Engine with a Mastra AI agent?

Import MCPClient from @mastra/mcp and initialize it with the Vinkius server URL. Spread the client's tools into your Mastra agent's tool configuration to expose the calculatemovingaverage function. This makes the math engine immediately available to your agent's decision-making loop.

Trigger automated trading workflows in Mastra AI using exact moving average calculations.

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Connect Moving Average Engine MCP to Mastra AI

Create your Vinkius account to connect Moving Average Engine to Mastra AI 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 Mastra AI

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Automated Trend Detection

The `calculate_moving_average` tool provides the exact mathematical foundation your Mastra AI agents need to evaluate market trends. Instead of letting an LLM guess if a crossover occurred, this tool calculates the exact point where the SMA or EMA lines intersect. This gives your workflows reliable triggers for automated execution. You can feed these outputs directly into Mastra's system of conditional steps. If the short-term average crosses above the long-term average, your workflow automatically triggers a buy order without human intervention.

Reliable Workflows with Mastra AI and this MCP Server

The `calculate_moving_average` tool integrates with Mastra's built-in retry engine to handle API hiccups gracefully. If a network delay occurs while calculating indicators, the workflow engine uses exponential backoff to retry the calculation. This prevents your automated trading logic from failing during high-volatility market events. Because the MCP Server runs in a sandboxed environment, your agent gets consistent execution speeds. You build complex, multi-step financial workflows knowing the underlying math will not fail or timeout.

Human-in-the-Loop Approvals

The `calculate_moving_average` tool supports Mastra's approval gates for high-value transactions. When your agent calculates a trend reversal, you can halt the workflow and require a manual sign-off before executing the trade. This combines deterministic math with human judgment. Mastra's framework handles the state management for these pauses. The calculated SMA or EMA values are preserved in the workflow context, waiting for your approval to proceed.

Setup guide

Set up Moving Average Engine MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Moving Average Engine tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "moving-average-engine-mcp-client",
  servers: {
    "moving-average-engine-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Moving Average Engine Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Moving Average Engine tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Moving Average Engine transactions"
);
console.log(result.text);

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Common questions about Moving Average Engine MCP in Mastra AI

Import `MCPClient` from `@mastra/mcp` and initialize it with the Vinkius server URL. Spread the client's tools into your Mastra agent's tool configuration to expose the `calculate_moving_average` function. This makes the math engine immediately available to your agent's decision-making loop.

Yes, Mastra's workflow engine automatically manages execution errors using built-in retries. If the `calculate_moving_average` tool encounters a temporary connection issue, Mastra retries the step with exponential backoff. This keeps your automated trading pipelines running without manual intervention.

The agent automatically extracts the required numerical arrays and period parameters from user queries. It then structures these inputs to match the schema of the `calculate_moving_average` tool. The engine validates the inputs, performs the math, and returns the indicators to the agent.

Yes, this MCP Server is built for live environments where mathematical accuracy is non-negotiable. Mastra's event-driven architecture allows you to run these calculations on a continuous loop, triggering actions the moment a trend shift is detected.

Yes, your financial data arrays are processed entirely in ephemeral memory. The Vinkius sandbox ensures that no inputs sent to the `calculate_moving_average` tool are logged or stored. Your proprietary trading data remains confidential throughout the entire workflow execution.

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