How to Use the Eden AI Alternative MCP in OpenAI Agents SDK

Q: How do I set up the Eden AI Alternative with OpenAI Agents SDK?

Install openai-agents first. Initialize the MCPServerStreamableHttp client pointing to your MCP Server endpoint. This exposes all 13 routing and document processing tools automatically.

Q: What is the best way to handle embeddings in this Eden AI Alternative and OpenAI Agents SDK MCP setup?

Use the createembedding tool to generate vectors from text inputs. You can find which models are currently active by calling listembeddingmodels directly from your python agent script.

Q: Can I restrict which tools the Eden AI Alternative exposes to my OpenAI Agents SDK deployment?

Yes, you can generate scoped access keys using the createcustomtoken tool. This ensures your deployment only has access to the specific endpoints required for its current task.

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Connect Eden AI Alternative MCP to OpenAI Agents SDK

Create your Vinkius account to connect Eden AI Alternative 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 Eden AI Alternative with OpenAI Agents SDK

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Route models dynamically via OpenAI Agents SDK

The `chat_completions` tool lets your agent swap between dozens of LLM providers on the fly without changing your underlying Python code. You point your agent to this single tool, and it handles the routing decisions based on your parameters. To keep conversations coherent across different backend models, the `create_stateful_response` tool maintains the dialogue history on the server. This prevents your agent from losing context when switching from one provider to another mid-task.

Execute OCR and translation jobs directly

The `universal_ai_sync` tool gives your agent immediate access to specialized engines for OCR, image generation, and translation. Instead of writing custom integration code for five different specialized APIs, you call this single tool. For larger inputs like audio files, the `universal_ai_async` tool starts background jobs that run without blocking your main execution thread. Your agent checks the status of these long-running tasks using `get_async_job` to retrieve the final output when ready.

Control your API budget with this MCP Server

The `check_credits` tool queries your remaining balance so your agent can stop execution before hitting hard limits. This keeps your production deployments from failing due to unexpected billing spikes. You manage the documents used for these tasks with `upload_file` and `delete_files` to keep your storage footprint clean. Tracking actual resource usage happens in real-time through the `monitor_consumption` tool.

Setup guide

Set up Eden AI Alternative 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 Eden AI Alternative tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Eden AI Alternative 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="Eden AI Alternative Agent",
            instructions="You have access to Eden AI Alternative 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 Eden AI Alternative MCP in OpenAI Agents SDK

Install `openai-agents` first. Initialize the `MCPServerStreamableHttp` client pointing to your MCP Server endpoint. This exposes all 13 routing and document processing tools automatically.

Use the `create_embedding` tool to generate vectors from text inputs. You can find which models are currently active by calling `list_embedding_models` directly from your python agent script.

Yes, you can generate scoped access keys using the `create_custom_token` tool. This ensures your deployment only has access to the specific endpoints required for its current task.

It replaces thousands of lines of boilerplate. You write one integration for this single MCP Server instead of maintaining separate clients for every translation, OCR, and LLM vendor.

Your documents go straight to isolated storage buckets. Tools like `upload_file` and `delete_files` give you direct control, and we purge the data as soon as you trigger the deletion endpoint.

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