How to Use the Gatling MCP in OpenAI Agents SDK

Q: How do I start a Gatling test with the OpenAI Agents SDK?

Your agent calls the startsimulation tool exposed by the MCP server. You'll need to provide the simulation ID, but your agent can find that by first calling listsimulations.

Q: Can my OpenAI agent check Gatling test results?

Yes. After starting a test with startsimulation, you get a run ID. Your agent then passes that ID to the getrun tool to fetch detailed results and statistics.

Q: What if a Gatling test goes wrong using my OpenAI agent?

Your agent can call abortsimulation with the run ID. This is useful for building automated workflows that stop runaway tests or clean up after a failed deployment.

Automate Gatling load tests from your OpenAI Agents SDK, with built-in safety guardrails for production pipelines.

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Connect Gatling MCP to OpenAI Agents SDK

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

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Trigger and Monitor Load Tests

Your OpenAI agent can `start_simulation` based on a PR comment or a schedule. It gets back a run ID, kicking off your performance test without any manual steps. Then, the agent can poll `get_run` to check the status and metrics. The SDK's guardrails are key here—you can set up a confirmation step before it actually calls `start_simulation`, preventing accidental, expensive test runs.

Manage Test Infrastructure

Before starting a test, your agent can check available resources. It calls `list_pools` to see if your load generators are ready, which avoids failed runs from saturated infrastructure. It's also about organization. An agent can use `list_teams` and `list_packages` to find the right simulation script for the right project. This is perfect for complex setups where different teams own different performance tests.

Automate Gates with your OpenAI MCP Server

This setup lets your agent become a gatekeeper for performance. You can build an agent that runs a performance test on every build, using `start_simulation` and then analyzing the results from `get_run`. If a test fails or metrics regress, the agent can automatically `abort_simulation` to save resources and post the results back to your team. The OpenAI dashboard gives you full tracing of why the agent made that call.

Setup guide

Set up Gatling 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 Gatling tools at runtime.
3

Create your Agent

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

Run the agent

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

Your agent calls the `start_simulation` tool exposed by the MCP server. You'll need to provide the simulation ID, but your agent can find that by first calling `list_simulations`.

Yes. After starting a test with `start_simulation`, you get a run ID. Your agent then passes that ID to the `get_run` tool to fetch detailed results and statistics.

Your agent can call `abort_simulation` with the run ID. This is useful for building automated workflows that stop runaway tests or clean up after a failed deployment.

This server handles the authentication and boilerplate. Your agent discovers the tools automatically, so you just tell it *what* to do, not *how* to make the API call.

The server processes metadata about your Gatling setup: simulation names, team IDs, and run statistics. It doesn't touch your application's user data. All API calls are made over encrypted connections from our ephemeral sandboxes.

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