How to Use the NIH RePORTER (Research Funding) MCP in OpenAI Agents SDK

Run production OpenAI Agents SDK pipelines that pull NIH funding data with built-in execution guardrails.

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Connect NIH RePORTER (Research Funding) MCP to OpenAI Agents SDK

Create your Vinkius account to connect NIH RePORTER (Research Funding) 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 NIH RePORTER (Research Funding) with OpenAI Agents SDK

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Claude

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Track the money first

`search_projects` connects your agent directly to the NIH RePORTER database to pull actual grant allocations, principal investigators, and organization names. Your agent inspects financial records and tracks where federal research capital flows without manual export steps. The OpenAI Agents SDK automatically registers this tool via HTTP stream. You pass the server instance inside the agent initialization, and the model immediately knows how to query the exact parameters it needs.

Audit research output

`search_publications` lets your agent retrieve every peer-reviewed paper tied to a specific NIH grant. This tool exposes the real-world output of funded research so you can measure actual scientific velocity. By using this MCP Server, your agent chains the funding input with the publication output. You get a direct line of sight from a dollar spent to a paper published.

Build safe production pipelines with OpenAI Agents SDK

This MCP integration uses the OpenAI Agents SDK guardrails to validate tool arguments before execution. If an agent tries to run a query that exceeds your specified boundaries, the SDK halts the execution before hitting the live API. You configure the server link once using the streamable HTTP parameters. Setting performance caching keeps the tool list warm, reducing latency on every run.

Setup guide

Set up NIH RePORTER (Research Funding) 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 NIH RePORTER (Research Funding) tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate NIH RePORTER (Research Funding) 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="NIH RePORTER (Research Funding) Agent",
            instructions="You have access to NIH RePORTER (Research Funding) tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by NIH RePORTER. 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 NIH RePORTER (Research Funding) MCP in OpenAI Agents SDK

Install the SDK and pass the server URL to the streamable HTTP parameter block. Use the async context manager to register the server directly in your agent configuration.

Yes. Your agent discovers both tools at runtime and uses the project IDs from the first tool to query the second tool.

The SDK relies on your HTTP transport configuration to handle retries. You should manage rate limits at the gateway level or wrap your agent execution in a retry loop.

Set the caching parameter to true when you initialize the server connection. This keeps the tool definitions in memory so the SDK does not fetch them on every turn.

Your search terms, project IDs, and investigator names remain strictly within your local Vinkius sandbox. This MCP setup processes these queries inside an isolated runtime and never stores or shares the raw parameters.

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