How to Use the Clarifai (Vision AI) MCP in OpenAI Agents SDK

Q: Can Clarifai (Vision AI) help my OpenAI Agents SDK agent see images?

Yes, by calling the predictmodel tool, your agent receives evaluation data from your vision models. It turns raw images into actionable classification output.

Plug Clarifai (Vision AI) into your OpenAI Agents SDK production pipeline for instant computer vision insights.

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Connect Clarifai (Vision AI) MCP to OpenAI Agents SDK

Create your Vinkius account to connect Clarifai (Vision AI) to OpenAI Agents SDK 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 OpenAI Agents SDK

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Trigger visual inference with OpenAI Agents SDK

Run the `predict_model` tool to get raw classification data from your image assets directly inside your agent loop. You get the exact bounding box results needed for your production safety checks. This keeps your agent logic clean while the Clarifai (Vision AI) MCP server handles the heavy lifting of image analysis. It's built for systems where you need to know exactly what the model saw before you move to the next step.

Map your Clarifai infrastructure

Call `list_apps` and `list_models` to see exactly what you have deployed in your environment. You'll see your current compute limits and available vision parameters without leaving your code. This gives your agent total visibility over your vision stack. It ensures your agent only targets models that are currently active and configured.

Trace your AI workflows

Use `list_workflows` to pull the structural data for your vision chains. It shows you the exact path your data takes through your AI limits. Your OpenAI agent uses this to verify that the pipeline is set up for success before it triggers a prediction. It prevents runtime errors by checking the structure first.

Setup guide

Set up Clarifai (Vision AI) MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Clarifai (Vision AI) tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Clarifai (Vision AI) tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Clarifai (Vision AI) tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Clarifai (Vision AI) Agent",
            instructions="You have access to Clarifai (Vision AI) tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Clarifai (Vision AI) MCP in OpenAI Agents SDK

Install the openai-agents package and initialize the MCPServerStreamableHttp class with your server URL. Pass this object directly into your Agent constructor's mcp_servers list to enable auto-discovery.

Yes, by calling the `predict_model` tool, your agent receives evaluation data from your vision models. It turns raw images into actionable classification output.

Your agent can use the data returned by the server to validate actions before they happen. If the vision tool returns an unexpected result, your guardrails will catch it before execution.

No. Your credentials stay on the Vinkius infrastructure, and only the endpoint token is used for communication.

The server processes visual nodes and classification metadata in an ephemeral sandbox. No raw image data is stored on Vinkius servers after the inference task finishes.

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