How to Use the Gatling MCP in LlamaIndex

Q: How do I configure LlamaIndex to use the Gatling tools?

Install llama-index-tools-mcp. Initialize a BasicMCPClient, wrap it in an McpToolSpec, and pass the async tool list to your FunctionAgent.

Q: Do I need to expose all MCP tools to the agent?

No. You restrict access using the allowedtools filter. If you only want an agent to read data, simply exclude the simulation start and abort commands.

Index your load testing history and query Gatling performance data directly through LlamaIndex RAG applications.

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Connect Gatling MCP to LlamaIndex

Create your Vinkius account to connect Gatling to LlamaIndex 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 LlamaIndex

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Turn Gatling MCP Server data into knowledge

LlamaIndex treats your Gatling load testing history as a searchable database. When your agent calls `list_simulations` and `list_runs`, it ingests those raw metrics into a vector store. You stop guessing about past performance. Developers can ask questions about previous baseline tests, and the agent retrieves exact run IDs and stats instead of hallucinating answers.

Ground load tests in historical context

Before executing a new Gatling test, your RAG application can pull the configuration of the last successful run using `get_simulation`. It compares the old setup against current requirements. If the parameters match, the agent safely executes `start_simulation`. This ensures your automated testing decisions are backed by actual historical data, not just static rules.

Query infrastructure state semantically

Setting up a massive Gatling load test requires available hardware. Your application pulls current capacity via `list_pools` and checks uploaded artifacts with `list_packages`. Users simply ask the agent if the QA environment is ready for a stress test. The system queries the index, verifies the pool status, and confirms before proceeding.

Setup guide

Set up Gatling MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Gatling MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Gatling tools.",
)
response = await agent.run("List recent Gatling data")

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Common questions about Gatling MCP in LlamaIndex

Install `llama-index-tools-mcp`. Initialize a `BasicMCPClient`, wrap it in an `McpToolSpec`, and pass the async tool list to your `FunctionAgent`.

It handles both reading and writing. Your agent can read historical metrics, and if authorized, use `start_simulation` or `abort_simulation` to actively manage test execution.

Raw API responses are hard to parse manually. Indexing the output of `get_run` lets you perform semantic searches across thousands of past performance tests instantly.

No. You restrict access using the `allowed_tools` filter. If you only want an agent to read data, simply exclude the simulation start and abort commands.

Your RAG application processes simulation parameters and performance metrics from `list_simulations`. We run the MCP connection inside a zero-trust V8 Isolate, meaning your authentication tokens never leak into the broader indexing pipeline.

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