How to Use the BlazeMeter MCP in AutoGen

Q: How do AutoGen agents coordinate a BlazeMeter test run?

One agent checks workspace limits using gettest while another verifies active masters with listmasters. They only call starttest once both approve.

Q: Can AutoGen agents automatically terminate a BlazeMeter run?

Yes. If the monitoring agent detects an issue via getreport, it proposes stopping the test. Once the group agrees, the execution agent calls stopmaster.

Q: How do I configure this AutoGen MCP Server with my BlazeMeter setup?

You register the server tools using mcpservertools and pass them to your AssistantAgent. The agents then discuss when and how to invoke each tool.

Let AutoGen agents debate performance metrics and coordinate BlazeMeter load tests.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect BlazeMeter MCP to AutoGen

Create your Vinkius account to connect BlazeMeter to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup BlazeMeter with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Consensus-Driven Test Execution

`start_test` triggers load tests only after your AutoGen agents agree on the configuration. A budget agent checks limits via `get_test` while a QA agent verifies the target project. Once they reach consensus, the execution agent starts the run. This prevents accidental, costly load tests on production environments.

Collaborative Incident Response via AutoGen

This AutoGen MCP Server lets agents monitor active runs using `get_master`. If metrics degrade, a security agent and a developer agent debate whether to abort. If they agree to halt, the execution agent calls `stop_master` to terminate the gateway. The entire decision process is logged in the conversation history.

Multi-Agent Workspace Auditing

`list_workspaces` and `list_projects` allow your agents to audit your BlazeMeter environments. One agent lists the projects, while another flags inactive ones. They use `get_user` to identify orphaned configurations. The agents then collaborate to clean up unused tests, keeping your workspace organized.

Setup guide

Set up BlazeMeter MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes BlazeMeter tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="BlazeMeter_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent BlazeMeter data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="BlazeMeter_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent BlazeMeter data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by BlazeMeter. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect BlazeMeter now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about BlazeMeter MCP in AutoGen

One agent checks workspace limits using `get_test` while another verifies active masters with `list_masters`. They only call `start_test` once both approve.

Yes. If the monitoring agent detects an issue via `get_report`, it proposes stopping the test. Once the group agrees, the execution agent calls `stop_master`.

You register the server tools using `mcp_server_tools` and pass them to your `AssistantAgent`. The agents then discuss when and how to invoke each tool.

It uses Streamable HTTP via the Vinkius gateway. This keeps the connection fast and stateless, ideal for multi-agent loops.

Yes. The Vinkius gateway runs the MCP Server in an isolated V8 sandbox. Only the structured JSON outputs from `get_report` are shared with the agents, keeping underlying infrastructure credentials hidden.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript