How to Use the Five9 QM MCP in Mastra AI

Q: How do I automate QA calibration with Five9 QM and Mastra AI?

Call listcalibrations to check ongoing alignment sessions, then trigger conditional workflows based on agent performance metrics. Mastra AI manages the execution logic and retries automatically if endpoints timing out.

Build automated, multi-step QA workflows that handle call calibration and evaluation retries with Mastra AI.

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Connect Five9 QM MCP to Mastra AI

Create your Vinkius account to connect Five9 QM 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 Five9 QM with Mastra AI

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Automate agent scorecards with Mastra AI workflows

Mastra AI runs automatic agent performance logging by triggering `create_evaluation` immediately after a customer call wraps up. The framework evaluates the text transcripts, maps them to compliance rules, and writes the final score to the call center database. If the Five9 QM API rejects the payload, Mastra AI uses the MCP Server connection to resubmit the evaluation with exponential backoff.

Trigger calibration sessions based on score drift

The framework monitors score consistency by pulling active alignment sessions via `list_calibrations` and matching them against Mastra AI agent decisions. When an evaluation score drifts from the baseline, Mastra AI triggers a calibration workflow to alert senior QA supervisors. This setup uses the MCP Server to bridge the gap between autonomous judgment and human oversight.

Route disputed evaluations to human moderators

You can build conditional logic that triggers `get_evaluation` when an agent disputes a grade within a Mastra AI routing step. The framework pulls the disputed scorecard details and routes the payload to a supervisor queue. The Mastra AI workflow pauses the automated pipeline, ensuring the disputed Five9 QM scorecard gets routed to a human supervisor for manual sign-off.

Setup guide

Set up Five9 QM 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 Five9 QM 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: "five9-qm-mcp-client",
  servers: {
    "five9-qm-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Five9 QM Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Five9 QM tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Five9 QM transactions"
);
console.log(result.text);

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Five9 QM. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Five9 QM MCP in Mastra AI

Call `list_calibrations` to check ongoing alignment sessions, then trigger conditional workflows based on agent performance metrics. Mastra AI manages the execution logic and retries automatically if endpoints timing out.

Yes, the framework uses built-in backoff strategies when tools like `list_qm_interactions` hit rate limits. This prevents your automated workflows from dropping call records during high-volume periods.

You register the server URL in your Mastra configuration and pass the tools directly into your agent definition. The agent then invokes tools like `get_qm_summary` autonomously based on workflow triggers.

Yes, Mastra AI supports human-in-the-loop validation for sensitive tools like `create_evaluation`. The workflow pauses and alerts a supervisor to confirm the score before writing to the database.

Completed scorecards containing agent IDs and performance ratings are processed inside isolated, ephemeral V8 sandboxes. Vinkius never persists your QA metrics or supervisor notes to disk.

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