How to Use the Wistia MCP in Mastra AI

Automate complex Wistia operations with Mastra AI's reliable workflow engine.

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Connect Wistia MCP to Mastra AI

Create your Vinkius account to connect Wistia 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 Wistia with Mastra AI

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Perplexity

Verify and Update Video Details

You can build a multi-step process to modify video assets. Start by getting the current metadata using `get_video_details`. Next, use that data in a conditional branch before calling `update_video_details` with new information. Mastra AI handles the entire sequence. If the update fails because of bad input, your workflow doesn't crash; it triggers a retry or notifies an admin automatically.

Analyze Account Performance Reliability

Build complex reports that rely on multiple data points. A flow could be: 1) Get account-wide stats via `get_account_analytics`. 2) Check project status using `list_projects`. 3) If the analytics show a dip, trigger an alert. Mastra AI ensures this chain completes even if one API endpoint is momentarily unavailable. This robust handling means your critical business logic always executes to completion.

Manage Full Project Lifecycle

Automate the creation and inspection of new Wistia projects. The workflow starts by calling `create_project`, which returns a project ID. Then, you use that ID in `get_project_details` to verify success. Mastra AI manages the state transitions for you. It's perfect for workflows that require absolute certainty, like onboarding or compliance checks.

Setup guide

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

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

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

Mastra AI handles the entire flow: it can list projects first with `list_projects`, then fetch details, and finally update specific metadata using `update_video_details`. The whole sequence is managed reliably.

The server touches video analytics and project/account metadata. Specifically, you'll be working with structured `video_stats` and general account metrics.

It uses automatic retries with exponential backoff. If one step—like fetching video details—fails due to a temporary network hiccup, the system waits and tries again until it succeeds or hits a defined failure point.

Absolutely. You can set up a conditional workflow: list all videos using `list_videos`, then loop through the results, calling `get_video_stats` for each one in turn.

The server touches account and video analytics data. You should ensure your workflow handles sensitive project metadata and usage statistics according to company policy, especially during retries.

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