How to Use the BlazeMeter MCP in OpenAI Agents SDK

Q: Can my OpenAI Agents SDK agent stop a runaway BlazeMeter test?

Yes. The agent calls stopmaster to halt the active run Gateway immediately. You can set safety guardrails in your Python code to require manual approval before this tool fires.

Q: How does the agent fetch test configurations?

It invokes gettest to retrieve explicit configurations tracing active Vault limit tests. The SDK parses the JSON payload directly into the agent's context window.

Trigger real-time load tests and pull performance metrics directly from OpenAI Agents SDK workflows.

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Connect BlazeMeter MCP to OpenAI Agents SDK

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

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Run load tests directly from OpenAI Agents SDK

The `start_test` tool lets your agent kick off irreversible load generation validations instantly during CI/CD runs. If things go sideways, the agent invokes `stop_master` to execute native Gateway shutdown logic and kill the run immediately. Built-in guardrails in your SDK validate these actions before execution. This prevents accidental multi-thousand-user tests from running indefinitely on your dime.

Map out testing environments via MCP Server

Your agent uses `list_workspaces` to inspect the Headless BlazeMeter Platform and find where your test configurations live. From there, `list_projects` extracts bounded projects so you never have to hunt for workspace IDs manually. Because OpenAI Agents auto-discover these MCP tools, you do not write glue code. The agent queries your setup, maps the hierarchy, and targets the exact test suite you need.

Pull raw performance metrics

The `get_report` tool pulls deep internal arrays to inspect Plan Math Reports right inside your chat thread using this MCP Server. You also get `list_masters` to pull active Master records and audit structured validation rules. This means you spot performance regressions during a deployment without opening the BlazeMeter UI. Everything streams straight to your terminal or agent dashboard.

Setup guide

Set up BlazeMeter 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 BlazeMeter tools at runtime.
3

Create your Agent

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

Run the agent

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

Install the SDK via pip and pass the Vinkius HTTP endpoint parameter to MCPServerStreamableHttp. Wrap it in an async context manager, then pass the server instance directly to your Agent constructor.

Yes. The agent calls stop_master to halt the active run Gateway immediately. You can set safety guardrails in your Python code to require manual approval before this tool fires.

No. Vinkius handles the authentication layer entirely, meaning your OpenAI Agents SDK agent needs only a single endpoint token to execute tools like list_tests.

It invokes get_test to retrieve explicit configurations tracing active Vault limit tests. The SDK parses the JSON payload directly into the agent's context window.

Vinkius runs the server in an isolated, zero-trust V8 sandbox. Your API tokens and test configuration payloads never persist on disk, and the ephemeral container wipes itself immediately after the request finishes.

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