Gatling MCP. Manage load testing from chat, not dashboards.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Gatling MCP Server manages full performance testing cycles. List simulations, trigger new load tests, and monitor runs all from your agent.
Check run stats, abort generators, or audit resource pools without leaving your chat window.
What your AI agents can do
Abort simulation
Immediately stops a running Gatling simulation.
Get run
Retrieves complete details and statistics for a specific Gatling test run.
Get simulation
Gets the full configuration details for a defined Gatling simulation scenario.
Trigger new performance test runs or immediately halt an active simulation using start_simulation and abort_simulation.
List all defined load scenarios (simulations) with list_simulations to understand what tests are available.
List past runs (list_runs) or retrieve full, detailed statistics for a specific run (get_run).
List load generator pools (list_pools) to confirm scaling capacity and check uploaded test packages (list_packages).
List registered teams (list_teams) and check overall resource consumption or quotas across your account.
Ask AI about this MCP
Supported MCP Clients
Gemini Waiting for input…
Gatling MCP Server: 10 Tools for Load Testing
Use these tools to control the entire lifecycle of performance testing, from listing scenarios to retrieving detailed run metrics.
019d75a2abort simulation
Immediately stops a running Gatling simulation.
019d75a2get run
Retrieves complete details and statistics for a specific Gatling test run.
019d75a2get simulation
Gets the full configuration details for a defined Gatling simulation scenario.
019d75a2list packages
Lists all test packages and artifacts uploaded to Gatling Enterprise.
019d75a2list pools
Lists the load generator pools and their regional capacity on Gatling Enterprise.
019d75a2list runs
Lists historical test runs for a given Gatling simulation.
019d75a2list simulations
Lists all load scenarios, showing names, IDs, and which teams own them.
019d75a2list teams
Lists all registered teams within your Gatling Enterprise account.
019d75a2list tokens
Lists your active API tokens for Gatling Enterprise.
019d75a2start simulation
Starts a new Gatling simulation run and returns the unique run ID.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Gatling, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
You've got your Gatling Enterprise account hooked up to your agent. Now you can run and manage full performance tests and high-scale load simulations right from your chat window. You'll use the agent to list all defined load scenarios with list_simulations, start a new test run using start_simulation, and immediately halt an active test with abort_simulation.
You can then list past runs with list_runs or grab full, detailed statistics for a specific run using get_run. You'll check resource capacity by listing load generator pools with list_pools and verifying uploaded test artifacts by listing packages with list_packages. You can also list all registered teams with list_teams and check your active API tokens with list_tokens.
How Gatling MCP Works
- 1 Subscribe to the Gatling MCP Server and provide your Gatling Enterprise API Token.
- 2 Your AI agent calls the necessary tool (e.g.,
list_simulations) based on your natural language request. - 3 The server executes the command, fetches the data from your Gatling account, and returns the structured result to your agent.
The bottom line is, your agent handles the API calls and data formatting, so you just talk to it.
Who Is Gatling MCP For?
Performance and QA Engineers, SREs, and DevOps teams. You're the person who gets the alert at 2 am that the checkout flow broke under load, and you need immediate, detailed metrics without opening a dozen dashboards. You need to audit resources and trigger tests on the fly.
Uses start_simulation to trigger specific load tests and then uses get_run to pull error counts and response time averages.
Uses list_pools and list_teams to verify infrastructure capacity and check team quota limits before a major deployment.
Uses list_packages to verify the latest uploaded test artifacts and get_simulation to review configuration details.
What Changes When You Connect
- Trigger tests instantly. Instead of navigating to the dashboard and clicking 'Run,' you simply ask your agent to
start_simulation. You get the run ID back right away. - Stop runaway tests fast. If a simulation starts generating too much load, use
abort_simulation. It gives you immediate control over your system resources. - See the numbers. Need to know if the API broke? Use
get_runto pull error rates, total requests, and average response times instantly. - Audit your setup. Use
list_poolsto verify load generator capacity across regions, orlist_packagesto confirm the latest version of your test code. - Keep track of history. Don't forget last week's test.
list_runsgives you a history of every execution for a simulation, saving you from manual record-keeping. - Control scope. Use
list_teamsto see which teams are registered and monitor who is consuming which resource quotas.
Real-World Use Cases
Debugging a Failed Checkout Flow
The QA Engineer knows the checkout API fails under stress, but the current dashboard is slow. They ask the agent to list_simulations to find the correct scenario, then start_simulation. Once the run completes, they use get_run to pull the error rate and average response time for immediate debugging.
Capacity Planning for Launch
The SRE needs to confirm if the infrastructure can handle a 10x traffic spike. They use list_pools to check the regional instance count and then use list_teams to confirm the total available credit quota before running a massive test.
Verifying Test Artifacts
A Developer finished updating a test script. Before running it, they use list_packages to verify the artifact was uploaded and the timestamp is correct. Then they use list_simulations to ensure the scenario exists before calling start_simulation.
Stopping a Rogue Test Run
A test run is running, but the traffic is spiking too high and hitting rate limits. The Ops Manager immediately tells the agent to abort_simulation. This stops the load generators instantly, preventing unnecessary resource consumption.
The Tradeoffs
Treating it like a database search
Trying to manually list everything by calling list_runs, then list_simulations, and then list_packages just to get a general idea of the system state.
→
Figure out your goal first. Need the history? Use list_runs. Need the scenarios? Use list_simulations. Don't call multiple list functions if one specific tool (like get_run or get_simulation) gets the exact data you need.
Assuming the run status
Asking the agent, 'Is the test running?' and waiting for a simple 'Yes' or 'No' without providing a specific run ID.
→
Always get the run ID first. Use list_runs to find the ID, then pass that ID to get_run to get the current status, peak VUs, and detailed progress.
Forgetting to check resources
Starting a massive test without knowing if the current load generator pool has enough capacity, leading to a failed test due to insufficient resources.
→
Always check resource capacity first. Call list_pools to check the region and instance count. Then, use start_simulation knowing the system can handle the load.
When It Fits, When It Doesn't
Use this if you need to manage the lifecycle of a performance test—from initial setup audit to live execution control. You need to know what tests are available (list_simulations), if the infrastructure can handle the load (list_pools), and how the test performed (get_run).
Don't use this if you just need to look at configuration files or static documentation. If your goal is only to see a list of names without triggering an action, you might be better off using a dedicated knowledge base or documentation tool. If you only need to see which API tokens are configured, use list_tokens—it’s a smaller scope than the full test lifecycle.
This server is for actions: start, stop, read metrics, and audit capacity.
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.
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Manual load testing means clicking through a dozen dashboards.
Right now, to check the performance of a new feature, you have to log into the Gatling dashboard. Then, you find the right simulation, click 'Run,' wait for it to finish, then copy the run ID, and finally open a separate tab to view the detailed request statistics. It's a copy-paste, click-through nightmare just to get one metric.
With the Gatling MCP Server, you tell your agent, 'Run the checkout test and give me the error rate.' You get the run started, the run ID, and the error rate—all in one response. It just works.
Gatling MCP Server: Manage load testing results with `get_run`.
You no longer need to manually cross-reference the simulation name, the run ID, and the metrics tab. Your agent handles the entire lookup. You ask for the stats for a specific run, and the agent pulls the total requests, error count, and average response time immediately.
It's not just faster; it's traceable. You get the precise, structured data you need without having to navigate the UI or worry about which tab you landed on. The data is ready to use.
Common Questions About Gatling MCP
How do I start a test using the `start_simulation` tool? +
You simply tell your agent to start the simulation. The agent handles the API call and immediately returns the unique run ID, so you know exactly what test is running.
Can I check the error rate for a past test using the `get_run` tool? +
Yes. Pass the specific run ID to get_run. It pulls the full run details, including the total error count and the error rate percentage.
What is the difference between `list_simulations` and `list_runs`? +
list_simulations shows the defined scenarios (the templates). list_runs shows the actual history of executed tests (the results). You need one to define the other.
How do I stop a test that's running too long using `abort_simulation`? +
Just tell your agent to abort the simulation. It sends the command to Gatling Enterprise and stops the load generators immediately, preventing unnecessary resource usage.
Do I need to check `list_pools` before starting a test? +
It's best practice. list_pools lets you audit the load generator capacity (regions and instance counts) before you run a massive test, preventing resource failure.
How do I check the available load generator capacity using `list_pools`? +
The list_pools tool shows all available load generator pools. This tells you the regions and instance counts, which lets you verify your overall scaling capacity before running a major simulation.
What information does `list_teams` provide about account usage? +
list_teams enumerates all registered teams in Gatling Enterprise. You can check member counts and view the credit quotas assigned to those teams, helping you manage usage.
If I need to see the original setup for a test, which tool should I use, `get_simulation` or `list_packages`? +
Use get_simulation to view the full setup and details of a specific load scenario. If you need to verify versions or upload timestamps for test materials, use list_packages.
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.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
More in this category
HackEDU (Security Journey)
Automate security training via HackEDU — manage users, track progress, and integrate vulnerability data directly from any AI agent.
Spider
High-performance Rust-powered web scraping and crawling — scrape, crawl, and search up to 100K+ pages/second with built-in anti-bot protection.
Scale AI
Automate high-quality data labeling and RLHF workflows via Scale AI — create projects, manage batches, and submit annotation tasks directly from your AI agent.
You might also like
Zenodo
Manage scientific research and datasets on Zenodo—create depositions, upload files, and search records directly from your AI agent.
Bookingmood
Manage your rental bookings via Bookingmood — track rentals, manage calendars, and handle guest contacts directly from any AI agent.
Clinical Medication Schedule Generator
Empower your AI Agent with deterministic medication scheduling. Effortlessly orchestrate strict multi-day dosage timelines offline, guaranteeing absolute privacy and temporal precision.