How to Use the PMC Open Access (PubMed Central) MCP in OpenAI Agents SDK

Q: How do I register the PMC Open Access (PubMed Central) tools in OpenAI Agents SDK?

Instantiate MCPServerStreamableHttp with the Vinkius endpoint. Pass this instance in the mcpservers list when initializing your agent.

Q: Can I use OpenAI Agents SDK to batch convert IDs with PMC Open Access (PubMed Central)?

Yes, you pass the convertids tool to your agent, which automates the looping logic. The SDK's built-in guardrails verify that the returned PMCIDs or DOIs match valid medical identifier formats.

Q: How does OpenAI Agents SDK handle rate limits when querying PMC Open Access (PubMed Central)?

You configure the MCPServerStreamableHttpParams to manage connection timeouts. If PubMed Central limits your requests, the SDK surfaces the error in your tracing dashboard so you can adjust your async execution rate.

Build production-grade biomedical agents with OpenAI Agents SDK that query PMC Open Access (PubMed Central) using native guardrails.

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MCP Servers — Included with Plan

Connect PMC Open Access (PubMed Central) MCP to OpenAI Agents SDK

Create your Vinkius account to connect PMC Open Access (PubMed Central) to OpenAI Agents SDK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

GDPR Included with Plan

Setup PMC Open Access (PubMed Central) with OpenAI Agents SDK

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Guarded ID mapping in OpenAI Agents SDK

The `convert_ids` tool translates PMCID, PMID, and DOI identifiers directly inside your OpenAI Agents SDK pipeline. When your agent runs this MCP tool, the SDK checks the output against your defined schema guardrails to prevent hallucinated citation formats from breaking downstream analysis. This setup runs the conversion inside an async context manager. By setting cacheToolsList to true, your agent avoids hitting the network twice when resolving IDs during multi-step clinical reasoning paths.

Automated citation output for OpenAI Agents SDK

The `export_citation` tool formats bibliographies for PubMed and PMC articles directly into your agent's execution loop. This MCP functionality ensures specialized OpenAI agents pass these structured citations to each other during handoffs, keeping the literature trail intact across different tasks. You track these data transfers on the OpenAI developer dashboard. This visibility lets you watch exactly how the agent parses the citation payload before delivering the final bibliography to your researchers.

Querying the MCP Server for full-text discovery

The `oa_discover` tool locates downloadable XML and PDF resources in the PMC Open Access Subset for your OpenAI Agents SDK tools array. This MCP Server allows your agent to grab open-access files and feeds them directly into custom processing pipelines. Because this MCP Server integrates with the HTTP streamable transport, your agent maintains a steady connection to PubMed Central. The SDK handles the underlying network calls, letting you focus on writing clean Python code.

Setup guide

Set up PMC Open Access (PubMed Central) 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 PMC Open Access (PubMed Central) tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate PMC Open Access (PubMed Central) 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="PMC Open Access (PubMed Central) Agent",
            instructions="You have access to PMC Open Access (PubMed Central) tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by PMC (PubMed Central). 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 PMC Open Access (PubMed Central) MCP in OpenAI Agents SDK

Instantiate `MCPServerStreamableHttp` with the Vinkius endpoint. Pass this instance in the `mcp_servers` list when initializing your agent.

Yes, you pass the `convert_ids` tool to your agent, which automates the looping logic. The SDK's built-in guardrails verify that the returned PMCIDs or DOIs match valid medical identifier formats.

You configure the `MCPServerStreamableHttpParams` to manage connection timeouts. If PubMed Central limits your requests, the SDK surfaces the error in your tracing dashboard so you can adjust your async execution rate.

Yes, you control tool exposure during agent initialization. You can expose only specific operations like `oai_get_record` or `oai_list_records` while hiding raw repository identification tools.

This server only touches open-access biomedical metadata, PMIDs, and PMCIDs. Vinkius runs the server in an isolated V8 sandbox, meaning your search queries and resolved citation lists are wiped immediately after the execution stream closes.

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