How to Use the Gatling MCP in CrewAI

Q: Can a CrewAI agent stop a running Gatling simulation?

Yes. A monitoring agent in your crew can call abortsimulation if the active run violates your performance SLA.

Q: How do CrewAI agents select the right Gatling simulation?

Your planner agent calls listsimulations to find the scenario matching your target test suite and passes the ID to the runner agent.

Q: Can CrewAI manage multiple Gatling teams?

Yes. Your admin agent uses listteams to discover team scopes and routes simulations to the correct organizational unit.

Q: How do agents verify load generator availability using the MCP Server?

Agents query listpools to check available infrastructure before triggering any new performance test runs.

Deploy a crew of autonomous agents to run, monitor, and scale your Gatling load tests in CrewAI.

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Connect Gatling MCP to CrewAI

Create your Vinkius account to connect Gatling to CrewAI 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 CrewAI

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Deploy a Gatling MCP Server crew in CrewAI

The `start_simulation` tool allows your lead QA agent to kick off performance tests based on the latest code commit. While that agent starts the test, a specialized monitor agent uses `get_run` to track live statistics. If metrics exceed acceptable limits, a moderator agent immediately calls `abort_simulation`. This collaborative setup prevents runaway resource usage without requiring manual developer intervention.

Analyze historical runs autonomously

The `list_runs` tool gives your analysis agent access to past execution history to detect long-term performance drift. The agent compares previous run metrics against the current `get_run` output to find regressions. Your CrewAI agents use this data to write markdown summaries. This MCP Server makes it easy for the agents to pull raw JSON metrics and translate them into actionable engineering tasks.

Coordinate pools and packages

The `list_pools` tool provides current generator capacity to your resource allocation agent. This agent coordinates with the deployment agent, which verifies compiled code using `list_packages`. By checking `list_teams` first, the crew ensures that simulations are run under the correct billing department. The entire process runs autonomously, handling setup, execution, and cleanup.

Setup guide

Set up Gatling MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Gatling tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Gatling Analyst",
    goal="Access and analyze Gatling data via MCP.",
    backstory="Expert analyst with direct Gatling access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Gatling transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

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

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Gatling Analyst",
        goal="Access and analyze Gatling data via MCP.",
        backstory="Expert analyst with direct Gatling access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Gatling transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

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 Gatling. 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.

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Common questions about Gatling MCP in CrewAI

Yes. A monitoring agent in your crew can call `abort_simulation` if the active run violates your performance SLA.

Your planner agent calls `list_simulations` to find the scenario matching your target test suite and passes the ID to the runner agent.

Yes. Your admin agent uses `list_teams` to discover team scopes and routes simulations to the correct organizational unit.

Agents query `list_pools` to check available infrastructure before triggering any new performance test runs.

Yes. This server handles raw run metrics and package details within an ephemeral, zero-trust V8 sandbox. Your data is fetched on demand and is never stored by the Vinkius platform.

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