How to Use the Gatling MCP in Pydantic AI

Q: How does Pydantic AI make using the Gatling MCP Server safer?

Every response from a Gatling tool like getrun is automatically parsed into a Pydantic model. If the data doesn't match the expected schema, it raises an error instead of letting your agent proceed with bad data.

Q: Can I use Pydantic AI to start a Gatling test and check the results?

Yes. Your agent calls startsimulation to get a run ID, then polls getrun for the results. Pydantic AI ensures the data at each step is valid, so your logic for analyzing the results is built on a solid foundation.

Q: What happens if the Gatling API changes when using Pydantic AI?

Your agent will fail loudly with a ValidationError. This is a feature, not a bug. It prevents your automation from making bad decisions based on data it doesn't understand.

Run type-safe Gatling operations from your Pydantic AI agent and get validated Pydantic models back.

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Connect Gatling MCP to Pydantic AI

Create your Vinkius account to connect Gatling to Pydantic AI 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 Pydantic AI

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Get Structurally-Guaranteed Test Results

When your agent calls `get_run`, the JSON response is automatically parsed and validated against a Pydantic model. If the Gatling API ever returns an unexpected field or a wrong data type, your code will raise a `ValidationError` immediately. This means no more silent failures or defensive coding to check if a key exists. You can trust that the data from `get_run` or `get_simulation` matches the schema you expect, every single time. It makes building reliable automation with an MCP Server straightforward.

Build Reliable Test Triggers with Pydantic AI

Start a load test with confidence. Your agent calls `start_simulation` and gets back a run ID. Pydantic AI ensures the response is what it's supposed to be, so you're not kicking off a workflow with bad data. Before you even start, you can build a chain of validated calls. Use `list_teams` to get a team ID, then `list_packages` to find the right package. Each step is type-checked, so the final call to `start_simulation` is guaranteed to have the correct, validated inputs.

Safely Manage the Test Environment

This MCP server exposes tools for managing your Gatling setup. Your Pydantic AI agent can safely call `list_pools` to check generator capacity or `list_simulations` to see what tests are available. The key is that the output of every tool is a validated Pydantic model. When your agent asks for a list of teams with `list_teams`, you get a list of `Team` objects, not a raw dictionary you have to parse and hope is correct.

Setup guide

Set up Gatling MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "gatling-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Gatling tools.",
)

result = await agent.run("List recent Gatling transactions")
print(result.output)

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Common questions about Gatling MCP in Pydantic AI

Every response from a Gatling tool like `get_run` is automatically parsed into a Pydantic model. If the data doesn't match the expected schema, it raises an error instead of letting your agent proceed with bad data.

Yes. Your agent calls `start_simulation` to get a run ID, then polls `get_run` for the results. Pydantic AI ensures the data at each step is valid, so your logic for analyzing the results is built on a solid foundation.

Your agent will fail loudly with a `ValidationError`. This is a feature, not a bug. It prevents your automation from making bad decisions based on data it doesn't understand.

Yes, it's model-agnostic. You can use this Gatling MCP Server with an agent powered by OpenAI, Anthropic, Gemini, or a local model, and you'll still get the same type-safe validation on every tool call.

The server only ever touches metadata related to your Gatling Enterprise configuration. This includes simulation names, run IDs, and performance metrics. It never accesses your core application data, and every connection is encrypted and isolated.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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