How to Use the Cacheflow MCP in Mastra AI

Build resilient sales workflows in Mastra AI with Cacheflow, handling proposal approvals and CRM syncs automatically.

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Connect Cacheflow MCP to Mastra AI

Create your Vinkius account to connect Cacheflow 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 Cacheflow with Mastra AI

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Perplexity

Mastra AI agents running Cacheflow tools

Assign `list_proposals` and `list_customers` to your agents to automate your sales research. The agent fetches the data and decides the next step based on your logic. Branching is easy when the agent knows exactly what to look for. You define the condition, and the agent executes the tool to get the answer.

Automated retries for Cacheflow in Mastra AI

Use the workflow engine to wrap `sync_to_crm` in a retry block. If the CRM connection drops, the agent waits and tries again without your input. This keeps your pipeline data clean. You don't have to worry about one-off failures breaking your entire sales process.

Human-in-the-loop with Mastra AI and Cacheflow

Require manual approval for `create_proposal` calls before they go live. The agent waits for your signal before triggering the actual record creation. It balances speed with control. You oversee the high-stakes parts of the deal while the agent does the heavy lifting on the backend.

Setup guide

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

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

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

Import the MCP client into your agent definition. Spread the tools directly into the agent's tool list to make them available.

Absolutely, define your workflows with branching steps. If a proposal isn't approved, you can tell the agent to notify you or try a different approach.

Yes, the server runs fine as part of your Mastra deployment. Just ensure your endpoint is reachable by your cloud environment.

Enable the requireToolApproval setting in your client config. This forces the agent to stop and ask for your sign-off on sensitive actions.

The integration uses scoped tokens for every tool call. Your proposal data is isolated within the workflow execution context and discarded after the task completes.

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