Can my agent start a Gatling simulation run via chat?

Yes. Use the 'start_simulation' tool with the specific Simulation ID. The agent will command the Gatling Enterprise infrastructure to begin the load test and return a unique run ID for tracking.

How do I check the request statistics for a completed test run via chat?

Use the 'get_run' tool. Provide the Run ID. Your agent will fetch the full details of the execution, including request stats, error counts, and the virtual user peak reached during the test.

Can I see my credit quotas and team member counts through the agent?

Absolutely. Use the 'list_teams' tool. Your agent will enumerate the teams registered in your Gatling Enterprise account and monitor their member counts and available credit quotas natively.

Gatling MCP Server for LangChain 10 tools — connect in under 2 minutes

Name: Gatling
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Gatling through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "gatling": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Gatling, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Gatling MCP Server

Connect your Gatling Enterprise account to any AI agent and take full control of your performance testing and high-scale load simulation through natural conversation.

LangChain's ecosystem of 500+ components combines seamlessly with Gatling through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Simulation Orchestration — List all Gatling simulations defining load scenarios and retrieve IDs, class names, and team associations natively
Live Test Execution — Trigger new performance test runs on Gatling Enterprise infrastructure and retrieve unique run IDs flawlessly
Test Run Monitoring — Track execution progress, statuses, and peak virtual user (VU) counts for ongoing or completed simulations synchronously
Detailed Stats Retrieval — Access full run details including request statistics, error counts, and injection start/end times limitlessly
Team & Quota Oversight — Enumerate teams registered in Gatling Enterprise and monitor member counts and credit quotas securely
Artifact Management — List uploaded test packages and artifacts to verify versions and upload timestamps across your environment
Resource Pool Auditing — Retrieve the list of load generator pools, identifying regions and instance counts to verify scaling capacity
Autonomous Aborting — Stop all load generators for a running simulation immediately to manage system resources and prevent overruns

The Gatling MCP Server exposes 10 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Gatling to LangChain via MCP

Follow these steps to integrate the Gatling MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Gatling via MCP

Why Use LangChain with the Gatling MCP Server

LangChain provides unique advantages when paired with Gatling through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Gatling MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Gatling queries for multi-turn workflows

Gatling + LangChain Use Cases

Practical scenarios where LangChain combined with the Gatling MCP Server delivers measurable value.

RAG with live data: combine Gatling tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Gatling, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Gatling tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Gatling tool call, measure latency, and optimize your agent's performance

Gatling MCP Tools for LangChain (10)

These 10 tools become available when you connect Gatling to LangChain via MCP:

abort_simulation

Abort a running Gatling simulation

get_run

Get full details of a Gatling run

get_simulation

Get full details of a Gatling simulation

list_packages

List uploaded packages/artifacts on Gatling Enterprise

list_pools

List load generator pools on Gatling Enterprise

list_runs

List runs for a Gatling simulation

list_simulations

Simulations define load scenarios with VU populations. Returns names, IDs, class names, and team associations. List all simulations on Gatling Enterprise

list_teams

List teams on Gatling Enterprise

list_tokens

List API tokens on Gatling Enterprise

start_simulation

Returns run ID. Start a Gatling simulation run

Example Prompts for Gatling in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Gatling immediately.

"List all simulations on Gatling Enterprise"

"Start simulation 'abc-123'"

"Show me the stats for run 'run_xyz789'"

Troubleshooting Gatling MCP Server with LangChain

Common issues when connecting Gatling to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Gatling + LangChain FAQ

Common questions about integrating Gatling MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.