How to Use the Deep Talk MCP in Mastra AI

Q: How does Deep Talk handle processing failures in Mastra AI?

listprocessingtasks monitors active NLP runs. If a task fails, Mastra's built-in workflow engine retries the operation automatically using exponential backoff.

Build resilient TypeScript workflows to automatically group support tickets and extract topics using Mastra AI.

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Connect Deep Talk MCP to Mastra AI

Create your Vinkius account to connect Deep Talk 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 Deep Talk with Mastra AI

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Classify tickets with Mastra AI and MCP

`list_conversation_clusters` exposes unsupervised machine learning groups and representative keywords from your customer support logs. Mastra AI agents use these clusters to trigger specific backend workflows based on ticket trends. You can write conditional branching logic that checks cluster sizes. If a cluster grows too fast, your workflow automatically notifies your product team.

Select NLP models dynamically

`list_available_nlp_models` identifies the active categorization engines available for your datasets. Your Mastra workflows use this list to select the correct model for sentiment or intent analysis. The workflow engine retries failed classification tasks with exponential backoff. If a model times out, Mastra falls back to an alternative engine from the list.

Monitor connected sources

`list_connected_sources` tracks synchronization status and ingestion volumes from platforms like Zendesk or Intercom. Your background agents verify these connections before starting any scheduled analysis. If a source disconnects, the agent pauses downstream tasks. It uses `get_dataset_metadata` to verify if the existing dataset is complete enough to proceed.

Setup guide

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

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

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

Install @mastra/mcp@latest and instantiate the client with your Vinkius endpoint. Retrieve the tools using mcpClient.listTools() and spread them directly into your agent's tool configuration.

Yes, Mastra supports human-in-the-loop workflows. Mastra's MCP integration allows you to configure requireToolApproval on tools like list_processing_tasks to verify runs before they execute.

list_processing_tasks monitors active NLP runs. If a task fails, Mastra's built-in workflow engine retries the operation automatically using exponential backoff.

Vinkius hosts the server, giving you a single HTTP endpoint. You can deploy your Mastra agent to any cloud provider with a single command.

Your customer conversation data travels through encrypted HTTPS connections directly to the Vinkius sandbox. No support files are ever stored on Vinkius after execution, keeping your records private.

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