How to Use the Uselessfacts MCP in OpenAI Agents SDK

Q: How does Uselessfacts work with OpenAI Agents SDK?

Your agent auto-discovers the MCP tools, including getfact, right out of the box. You don't write any boilerplate code to connect the server; it just shows up for your agent client.

Q: If I need a German fact, which tool should use Uselessfacts with OpenAI Agents SDK?

For German facts, you'll want to use getrandomfact(language='de') or gettodayfact(language='de'). Both tools accept the language parameter.

Make your agents smarter with Uselessfacts | OpenAI Agents SDK

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Connect Uselessfacts MCP to OpenAI Agents SDK

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

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Get Specific Facts via `get_fact`

The `get_fact(id)` tool pulls a specific fact by its ID. You just give it the number, and your agent gets the corresponding useless data point. This is great for when you know exactly what kind of random trivia you need. It keeps the query tight and focused on one piece of info.

Discover Random Trivia with MCP Server

Call `get_random_fact()` to pull a completely arbitrary useless fact. You can also specify the language—just pass 'de' for German or leave it out for English. This is perfect for breaking up a long chain of thought, giving your agent something light to reference.

Know Today’s Fact with `get_today_fact`

Use the `get_today_fact()` tool to grab the useless fact relevant for the current date. Again, you control the language by passing 'en' or 'de'. This is a simple check that gives your agent timely context without needing an external calendar API.

Setup guide

Set up Uselessfacts 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 Uselessfacts tools at runtime.
3

Create your Agent

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

Run the agent

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

Your agent auto-discovers the MCP tools, including `get_fact`, right out of the box. You don't write any boilerplate code to connect the server; it just shows up for your agent client.

The MCP Server doesn't let you filter by category, but you control what happens with its output. You can design your agents to only act on facts related to specific topics.

This MCP Server handles fact strings and IDs (integers). These are simple, read-only pieces of text and numbers that your agent reads to execute its task.

For German facts, you'll want to use `get_random_fact(language='de')` or `get_today_fact(language='de')`. Both tools accept the language parameter.

The MCP Server is designed to be efficient. Since it only reads small amounts of data, its response time supports rapid agent execution in a deployed environment.

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