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 Pydantic AI 10 tools — connect in under 2 minutes

Name: Gatling
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Gatling through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Gatling "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Gatling?"
    )
    print(result.data)

asyncio.run(main())

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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.

Pydantic AI validates every Gatling tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Gatling MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Gatling with type-safe schemas

Why Use Pydantic AI with the Gatling MCP Server

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

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Gatling integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Gatling connection logic from agent behavior for testable, maintainable code

Gatling + Pydantic AI Use Cases

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

Type-safe data pipelines: query Gatling with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Gatling tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Gatling and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Gatling responses and write comprehensive agent tests

Gatling MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Gatling to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Gatling + Pydantic AI FAQ

Common questions about integrating Gatling MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Gatling MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.