How to Use the Refiner MCP in Mastra AI

Q: How do I load Refiner MCP Server tools into a Mastra AI agent?

Install @mastra/mcp@latest and initialize the client with your Vinkius endpoint URL. Call mcpClient.listTools() and spread the resulting toolset directly into your agent's configuration object.

Q: Does Mastra AI handle rate limits when calling Refiner?

Yes, Mastra has built-in exponential backoff. If listrefinercontacts hits an API rate limit, the workflow engine automatically retries the call until the request succeeds.

Q: Can I require approvals before Mastra AI modifies a Refiner user?

Absolutely. You can set requireToolApproval on identifyrefineruser so your team must manually confirm any profile updates before they are pushed to your live customer database.

Q: How do I test the connection in my workflow?

You can write a simple test workflow that triggers checkrefinerstatus. If it returns a healthy status, your agent is ready to query and update your survey data.

Q: Is our SaaS user contact data safe with Mastra and Vinkius?

Highly secure. Your customer emails and traits fetched via getrefinercontact are transmitted through a zero-trust, ephemeral V8 sandbox. No contact records are ever cached or stored on the Vinkius platform.

Run automated feedback workflows with Mastra AI and the Refiner MCP Server to route churn signals instantly.

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Connect Refiner MCP to Mastra AI

Create your Vinkius account to connect Refiner 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 Refiner with Mastra AI

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Automate Churn Responses with Mastra Workflows

When a user submits negative feedback, you can't afford to wait. Mastra AI uses this MCP Server to trigger automated workflows the second a bad score drops. The agent runs `list_refiner_responses` to catch low scores, then branches the workflow based on the exact sentiment. If a customer signals they are about to cancel, the workflow automatically runs `get_refiner_contact` to pull their details. From there, Mastra routes the ticket to your high-priority Slack channel, retrying the step automatically if your internal webhook fails.

Sync User Segments Dynamically

Keeping user lists up to date across your stack is usually a pain. With Mastra AI, you can build an agent that monitors your database and calls `identify_refiner_user` to update custom attributes in real-time. The agent uses `list_refiner_segments` to verify the user landed in the correct bucket. Since Mastra handles complex branching, you can set up a rule that only runs this sync for accounts with active subscriptions, saving API quota.

Human-in-the-Loop Event Tracking

You don't always want your agent updating user tracking data without supervision. Mastra AI lets you require manual approval before running critical tools like `track_refiner_event`. When the agent decides to trigger a custom event, Mastra pauses the workflow and asks for your go-ahead. Once approved, the tool executes, updating the user's timeline inside Refiner so your product team sees accurate product usage data.

Setup guide

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

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

const result = await agent.generate(
  "List recent Refiner 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 Refiner. 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 Refiner MCP in Mastra AI

Install `@mastra/mcp@latest` and initialize the client with your Vinkius endpoint URL. Call `mcpClient.listTools()` and spread the resulting toolset directly into your agent's configuration object.

Yes, Mastra has built-in exponential backoff. If `list_refiner_contacts` hits an API rate limit, the workflow engine automatically retries the call until the request succeeds.

Absolutely. You can set `requireToolApproval` on `identify_refiner_user` so your team must manually confirm any profile updates before they are pushed to your live customer database.

You can write a simple test workflow that triggers `check_refiner_status`. If it returns a healthy status, your agent is ready to query and update your survey data.

Highly secure. Your customer emails and traits fetched via `get_refiner_contact` are transmitted through a zero-trust, ephemeral V8 sandbox. No contact records are ever cached or stored on the Vinkius platform.

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