How to Use the Jiminny MCP in Mastra AI

Q: Can I build conditional coaching loops with Jiminny and Mastra AI?

Absolutely. You can write a workflow that checks getcoachingstats and branches based on the score, routing high-performing reps to one track and those needing help to another.

Build automated sales coaching workflows with Jiminny and Mastra AI.

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Connect Jiminny MCP to Mastra AI

Create your Vinkius account to connect Jiminny 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 Jiminny with Mastra AI

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Trigger coaching loops with Mastra AI

The `get_coaching_stats` tool allows your Mastra agent to monitor rep performance and trigger automated coaching workflows when metrics dip. If a rep's stats fall below a threshold, the workflow branches to notify their manager with specific coaching recommendations. You can use `get_user` to find the rep's manager and route the notification. Mastra's built-in workflow engine handles the logic, retrying the API call if the network hiccups.

Route sales action items using this MCP Server

This MCP Server provides `get_action_items` to extract commitments made during customer calls. Mastra AI can ingest these items and execute conditional workflows, like creating tasks in your CRM or sending Slack alerts to the account team. If the CRM write fails, Mastra's exponential backoff ensures the action item is retried. Your agents use `get_activity` to verify the call context before executing the next step in the pipeline.

Audit team performance on a schedule

The `list_activities` tool lets your agent pull all calls from the previous day to run bulk audits. Mastra AI coordinates this multi-step process, calling `get_transcript` for each call and analyzing the text for coaching opportunities. Using `list_users` and `list_teams`, the agent groups the audit results and compiles a report. The workflow engine ensures that even if you have hundreds of calls, the process runs reliably without hitting rate limits.

Setup guide

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

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

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

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Common questions about Jiminny MCP in Mastra AI

Mastra AI has a built-in retry engine with exponential backoff. If `get_call_stats` fails due to rate limits, the framework automatically retries the tool execution without crashing your workflow.

Absolutely. You can write a workflow that checks `get_coaching_stats` and branches based on the score, routing high-performing reps to one track and those needing help to another.

You initialize the MCP client with your Vinkius endpoint and spread the tools directly into your agent's tool configuration. The framework auto-detects the transport and registers tools like `list_activities` instantly.

Yes, you can use Mastra's human-in-the-loop feature to pause the workflow after running `get_action_items`. A manager can approve the tasks before the agent writes them to your external systems.

Your Jiminny call transcripts, action items, and coaching metrics are protected by Vinkius's zero-trust architecture. The data is processed in ephemeral sandboxes, meaning no call content is stored or used for training models.

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