How to Use the Outlier Detection Engine MCP in Mastra AI

Q: How do I integrate the Outlier Detection Engine into a Mastra AI workflow?

Add the server to your MCPClient configuration. The tool definitions will automatically appear in your agent's available toolset for use in any step.

Q: Does the Outlier Detection Engine support human-in-the-loop for Mastra AI?

Absolutely. Use the requireToolApproval setting in your Mastra agent to pause the workflow whenever the Outlier Detection Engine identifies a potential anomaly.

Build reliable data pipelines with the Outlier Detection Engine for Mastra AI agents.

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Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Outlier Detection Engine MCP to Mastra AI

Create your Vinkius account to connect Outlier Detection Engine to Mastra AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Outlier Detection Engine with Mastra AI

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Automated anomaly flagging in workflows

Integrate the `detect_outliers` tool into your Mastra agent chains to catch bad data before it hits your database. It handles the heavy lifting of statistical thresholding. If the engine identifies an anomaly, your workflow can trigger a branch to notify an admin or quarantine the record. It's built for logic, not just chat.

Resilient multi-step data processing

Mastra AI agents often handle complex sequences. By using this server, you ensure that every step of your pipeline operates on clean, validated inputs. Configure your agents to run the check automatically. If the `detect_outliers` tool flags a value, the agent stops the process and waits for human approval.

Deterministic logic for agent decisions

Agents perform best when they have concrete rules. Giving them access to IQR and Z-Score math prevents the agent from making up data quality assessments. Everything happens locally. You define the agent behavior, and the server provides the mathematical evidence needed to support those actions.

Setup guide

Set up Outlier Detection 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 Outlier Detection 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: "outlier-detection-engine-mcp-client",
  servers: {
    "outlier-detection-engine-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

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

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Common questions about Outlier Detection Engine MCP in Mastra AI

Add the server to your `MCPClient` configuration. The tool definitions will automatically appear in your agent's available toolset for use in any step.

Absolutely. Use the `requireToolApproval` setting in your Mastra agent to pause the workflow whenever the Outlier Detection Engine identifies a potential anomaly.

The calculations are highly optimized for speed. Processing 50,000 rows takes milliseconds, so your agents remain responsive during batch operations.

Security is enforced at the transport level. Your raw numeric data remains inside your infrastructure, and only the tool results are shared with your agent session.

You can. If the tool returns a high count of outliers, you can configure your workflow logic to retry the data collection step or log a failure.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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