How to Use the TrueFoundry MCP in OpenAI Agents SDK

Q: How do I check deployment status with TrueFoundry and OpenAI Agents SDK?

You call truefoundrygetdeploymentstatus to emit detailed metric states on the orchestration matrix bounds. This tells your agent if a service is ready for use by the OpenAI Agents SDK.

Q: Can I list all available tools with TrueFoundry and OpenAI Agents SDK?

Yep. Use truefoundrylistmcpservers to get a registry mapping of every logical MCP Tool in TrueFoundry. Your agent can read this before deciding which tool it needs.

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Connect TrueFoundry MCP to OpenAI Agents SDK

Create your Vinkius account to connect TrueFoundry 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 TrueFoundry with OpenAI Agents SDK

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Deploying Services with MCP Server

You can spawn a new backend container logical process using `truefoundry_deploy_mcp_server` and monitor the result. This lets your agent manage its own required services before running tasks. Use `truefoundry_list_deployments` to get an array of all currently running backend topologies mapped to the team. It's critical for ensuring the agent doesn't try to use a service that failed to spin up.

Interacting with the Gateway Model

The `truefoundry_run_gateway_chat` tool lets your agent push an explicit model query string through TrueFoundry. This handles mapping the chat parameter correctly for models like g., openai/gpt-4o). Before calling it, check available options using `truefoundry_list_gateway_models`. You'll see every accessible foundation model listed from the unified AI gateway.

Generating Embeddings

When your agent needs to perform semantic search or RAG, it calculates vectors securely using `truefoundry_generate_embeddings`. The process uses a unified abstraction layer for consistency. This tool doesn't just create embeddings; it provides the necessary inputs for specialized agents that need contextual data before executing complex logic.

Setup guide

Set up TrueFoundry 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 TrueFoundry tools at runtime.
3

Create your Agent

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

Run the agent

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

You call `truefoundry_get_deployment_status` to emit detailed metric states on the orchestration matrix bounds. This tells your agent if a service is ready for use by the OpenAI Agents SDK.

Yep. Use `truefoundry_list_mcp_servers` to get a registry mapping of every logical MCP Tool in TrueFoundry. Your agent can read this before deciding which tool it needs.

You extract the exact JSON metadata of a registered TrueFoundry tool schema using `truefoundry_get_mcp_server_info`. This lets your agent validate what functions are available.

Yes. The gateway is designed to handle various models, allowing you to push explicit queries via `truefoundry_run_gateway_chat` for different LLM types.

The server touches model schema JSON metadata. This includes structured definitions of what inputs a tool expects and what it returns, ensuring your agent knows exactly what to send.

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