How to Use the Newslit MCP in OpenAI Agents SDK

Q: Can Newslit handle large-scale data in OpenAI Agents SDK workflows?

Yes. By using liststories to fetch only the necessary segments, you keep memory usage predictable. It prevents your agent from hitting token limits during high-volume tracking.

Q: What is the best way to handle report updates in OpenAI Agents SDK?

Run updatereport whenever your agent detects new metadata. This approach ensures your tracking remains precise as the news cycle shifts.

Build production media agents with Newslit and the OpenAI Agents SDK to automate your PR tracking lifecycle.

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Connect Newslit MCP to OpenAI Agents SDK

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

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Automate news reporting in OpenAI Agents SDK

Use `create_report` to generate structured media summaries directly within your agent workflow. This MCP Server handles the payload formatting so your agent stays focused on the content. Your agent can trigger `list_reports` to check existing files and ensure no work is duplicated. It keeps your pipeline clean without manual intervention.

Fetch press stories for your OpenAI agent

Call `list_stories` to pull relevant media mentions into your agent's context window. It feeds raw data directly into your analysis loop for immediate processing. Once the data is in, use `get_report` to verify the findings. This ensures your agent acts on accurate, verified information every time.

Maintain report state with Newslit tools

Manage your media footprint using `update_report` and `delete_report`. These tools let your agent refine data as stories evolve or expire. It keeps your records current without requiring you to switch tabs. Your agent manages the entire lifecycle of your media tracking database.

Setup guide

Set up Newslit 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 Newslit tools at runtime.
3

Create your Agent

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

Run the agent

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

The server exposes endpoints that your agent discovers automatically. You simply pass it to the constructor, and the agent maps the functions to its internal logic.

Yes. By using `list_stories` to fetch only the necessary segments, you keep memory usage predictable. It prevents your agent from hitting token limits during high-volume tracking.

Your data remains local to your environment. The MCP Server acts as a controlled gateway, only touching the specific news records you authorize it to access.

Run `update_report` whenever your agent detects new metadata. This approach ensures your tracking remains precise as the news cycle shifts.

It does. The implementation is built for non-blocking execution, allowing your agent to monitor news while performing other tasks.

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