How to Use the SleekFlow MCP in Mastra AI

Q: How do I verify contact details using SleekFlow and Mastra AI?

Call getcontactdetails within your workflow. If the initial call fails, Mastra AI will automatically retry fetching the data until it's available or alerts you that it can't proceed.

Q: Can I automate message replies using SleekFlow and Mastra AI?

Yes. You use sendmessage inside a defined workflow step. The system handles the transport layer, making sure your reply goes through correctly even if there are minor API hiccups.

Q: How does SleekFlow and Mastra AI handle conversation lists?

You use listconversations to pull all history. This data is then passed into the next step of your workflow, allowing you to analyze it or act on it immediately.

Q: Does this MCP Server support OAuth for Mastra AI?

Yes. The setup supports authProvider right out of the box. You can connect your enterprise accounts without worrying about refreshing tokens manually in your code.

Q: What types of data does the SleekFlow MCP Server touch regarding privacy?

The server touches contact details, including IDs, labels (listcontactlabels), and message content from listconversations.

Build reliable, multi-step customer workflows with Mastra AI's robust workflow engine.

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Create your Vinkius account to connect SleekFlow 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 SleekFlow with Mastra AI

Ask AI about this MCP

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Claude

Perplexity Vinkius runs on Gemini

Gemini

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Reliable Message Sending

Use `send_message` to send a reply through an MCP Server conversation. If the API call fails, Mastra AI handles retries automatically using exponential backoff. Your workflow doesn't stall because of rate limits.

Multi-Step Contact Logic

You can chain actions: first run `list_contacts` to find a customer ID, then use that ID with `get_contact_details`. Mastra AI ensures the whole sequence completes or retries until it works. It's solid failure handling.

State Management for Flows

The built-in workflow engine lets you check specific status changes via `list_automation_flows`. If a flow fails (e.g., due to invalid labels), Mastra AI can trigger a notification or switch to an alternate process.

Setup guide

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

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

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

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Common questions about SleekFlow MCP in Mastra AI

Call `get_contact_details` within your workflow. If the initial call fails, Mastra AI will automatically retry fetching the data until it's available or alerts you that it can't proceed.

Yes. You use `send_message` inside a defined workflow step. The system handles the transport layer, making sure your reply goes through correctly even if there are minor API hiccups.

You use `list_conversations` to pull all history. This data is then passed into the next step of your workflow, allowing you to analyze it or act on it immediately.

Yes. The setup supports `authProvider` right out of the box. You can connect your enterprise accounts without worrying about refreshing tokens manually in your code.

The server touches contact details, including IDs, labels (`list_contact_labels`), and message content from `list_conversations`.

Use it with your favorite AI tools

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