How to Use the Gatling MCP in AutoGen

Q: How do I load Gatling MCP tools into AutoGen?

Install autogen-extmcp. Use mcpservertools with your HTTP endpoint, and pass the resulting list into your AssistantAgent constructor.

Q: Can AutoGen agents read Gatling team configurations via MCP?

Yes. They can call listteams and listtokens to understand the organizational setup on Gatling Enterprise before deciding which test to run.

Q: How does the framework handle MCP schema conversion?

The McpToolAdapter automatically translates the server's JSON definitions into a format AutoGen understands. You write zero parsing code.

Deploy debating AutoGen agents that negotiate load testing parameters before triggering Gatling simulations.

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Setup Gatling with AutoGen

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Negotiate Gatling MCP Server parameters

AutoGen thrives on debating Gatling test parameters. You can build a system where a performance agent proposes a massive load test, while an infrastructure agent checks `list_pools` to verify capacity. They argue over the setup. The performance agent pulls requirements via `get_simulation`, but the infra agent pushes back if the requested load generators are busy. They reach consensus before anything runs.

Coordinate test execution as a team

Once the agents agree on a Gatling configuration, the designated executor calls `start_simulation`. A separate monitoring agent immediately takes over, polling `get_run` to watch the metrics roll in. This separation of concerns prevents a single agent from getting stuck. The team works together, with one checking `list_packages` for updates while another handles the actual run.

Enforce safety through consensus

Automated Gatling load testing needs safeguards. If the monitoring agent detects spiking error rates in `list_runs`, it alerts the group. A security or reliability agent can instantly veto the operation and trigger `abort_simulation`. You get autonomous testing with built-in, multi-perspective safety checks.

Setup guide

Set up Gatling 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 Gatling 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="Gatling_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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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="Gatling_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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Common questions about Gatling MCP in AutoGen

Install `autogen-ext[mcp]`. Use `mcp_server_tools` with your HTTP endpoint, and pass the resulting list into your `AssistantAgent` constructor.

Load testing impacts production systems. AutoGen lets you require consensus between a developer agent and a simulated QA lead before any heavy traffic hits your servers.

Yes. They can call `list_teams` and `list_tokens` to understand the organizational setup on Gatling Enterprise before deciding which test to run.

The `McpToolAdapter` automatically translates the server's JSON definitions into a format AutoGen understands. You write zero parsing code.

Your agents interact with load generator details and team IDs via `list_teams`. Every MCP server operates in an ephemeral, zero-trust sandbox, ensuring that sensitive test configurations disappear the moment the session ends.

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