How to Use the GroundX MCP in OpenAI Agents SDK

Q: How does GroundX prevent my OpenAI Agents SDK from getting stale data?

Your agent calls getingeststatus to verify that newly uploaded files are fully processed. Once GroundX finishes indexing, the agent immediately uses searchcontent to fetch the fresh context.

Q: Does this MCP Server support file uploads directly from OpenAI agents?

No, GroundX processes files via URLs or paths using ingestdocuments. Your agent provides the file location, and the server handles the actual data ingestion asynchronously.

GroundX feeds search-ready data directly to your OpenAI Agents SDK MCP Server setup to prevent hallucination in production workflows.

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Connect GroundX MCP to OpenAI Agents SDK

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

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Fast Document Ingestion for OpenAI Agents SDK

GroundX uses `ingest_documents` and `ingest_website` to pull raw content into isolated, search-ready containers. Your OpenAI agents trigger these tools directly during execution, bypassing custom ingestion pipelines entirely. The SDK caches this tool schema at startup, ensuring your agents instantly know how to structure ingestion payloads. You track the progress of these jobs using `get_ingest_status` within your agent's execution loop.

Structured RAG Search with Guardrails

This MCP Server exposes `search_content` to let your agents query processed data with high precision. Before running the search, the SDK validates the agent's intent, stopping bad queries before they hit your GroundX endpoint. Your agent uses `search_documents` to pull specific chunks based on metadata constraints. The OpenAI tracing dashboard logs every search call, letting you debug exactly what context was fed to the model.

Dynamic Bucket and Group Isolation

GroundX manages isolated data environments using `create_bucket` and `create_group` directly from your agent code. This MCP integration lets specialized OpenAI agents handle different buckets, handing off tasks to one another as data requirements shift. Your agent inspects available storage pools using `list_buckets` and `list_groups` to route queries correctly. This architecture prevents data leaks between different customer profiles in multi-tenant setups.

Setup guide

Set up GroundX 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 GroundX tools at runtime.
3

Create your Agent

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

Run the agent

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

Your agent calls `get_ingest_status` to verify that newly uploaded files are fully processed. Once GroundX finishes indexing, the agent immediately uses `search_content` to fetch the fresh context.

Yes, you configure this at the Agent constructor level by filtering the exposed tool list. This prevents a customer-facing agent from calling admin tools like `create_group` while keeping `search_documents` open.

The SDK manages execution queues, but you should also check `get_customer_info` to monitor your account limits. GroundX returns clean error codes that trigger standard SDK retry logic automatically.

No, GroundX processes files via URLs or paths using `ingest_documents`. Your agent provides the file location, and the server handles the actual data ingestion asynchronously.

GroundX processes your PDFs, web crawls, and search indexes in isolated sandboxes on Vinkius. Your OpenAI Agents SDK connects via a single secure token, ensuring no raw document data is exposed to public LLM training sets.

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