How to Use the Clarifai (Vision AI) MCP in Mastra AI

Q: How do I initialize Clarifai (Vision AI) inside Mastra AI?

Run npm install @mastra/mcp@latest. Instantiate MCPClient with your Vinkius URL, then spread mcpClient.listTools() into your agent configuration.

Q: How do I require human approval for computer vision tasks in Mastra AI?

Set requireToolApproval to true for the prediction tool. The workflow pauses and waits for a user to review the image classification before continuing.

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Connect Clarifai (Vision AI) MCP to Mastra AI

Create your Vinkius account to connect Clarifai (Vision AI) 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 Clarifai (Vision AI) with Mastra AI

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Execute Vision Pipelines via MCP Server

`predict_model` dispatches automated validation inference for explicit network predictions. When you build a Mastra AI workflow, this tool acts as the core processing node that evaluates visual data and returns classification arrays. Networks fail. If an image URL drops or the inference times out, the framework catches the error. You configure exponential backoff, and the agent tries the prediction again before routing the failure to a human-in-the-loop approval step.

Audit Model and Dataset Bounds

`list_datasets` identifies the precise physical bounds mapping your data structures. Your agent uses this to verify the training data source before deciding which visual nodes to trust. Following that check, `list_models` performs structural extraction of the computer vision parameters. The workflow branches based on whether a specific model version is active in the environment.

Validate Workflow Chains Dynamically

`list_workflows` retrieves the exact structural matching required to verify chained operations. Instead of guessing the sequence, your agent asks the MCP server for the correct pipeline configuration. `list_apps` then identifies the bounded applications managing your global compute limits. `list_concepts` extracts the explicitly attached semantic bounds, ensuring the chained output matches the expected taxonomy.

Setup guide

Set up Clarifai (Vision AI) 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 Clarifai (Vision AI) 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: "clarifai-vision-ai-mcp-client",
  servers: {
    "clarifai-vision-ai-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent Clarifai (Vision AI) 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 Clarifai. 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 Clarifai (Vision AI) MCP in Mastra AI

Run `npm install @mastra/mcp@latest`. Instantiate `MCPClient` with your Vinkius URL, then spread `mcpClient.listTools()` into your agent configuration.

Yes. The framework automatically handles network timeouts or rate limits from the inference endpoint by applying exponential backoff to the tool call.

Set `requireToolApproval` to true for the prediction tool. The workflow pauses and waits for a user to review the image classification before continuing.

You write a conditional branch in your workflow. The agent evaluates the returned tags and routes the execution path differently based on the classification results.

Training dataset IDs and model parameters process within an isolated zero-trust sandbox. Authentication requires only a single endpoint token, and the environment tears down completely once the workflow step finishes.

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