How to Use the LoadNinja (Real-Browser Load Testing) MCP in LlamaIndex

Q: How do I ingest LoadNinja (Real-Browser Load Testing) data into LlamaIndex?

You'll use the llama-index-tools-mcp package to connect the MCP Server. Once connected, use the McpToolSpec to feed tool outputs from gettestrunstats directly into your document indexers.

Q: Does LlamaIndex query active test runs using this server?

Yes, your agent calls listtestruns to get a list of active runs, then indexes their current completion status to provide real-time updates through your search interface.

Q: Does LlamaIndex support running new tests via the LoadNinja (Real-Browser Load Testing) MCP Server?

Yes, the agent uses the runscenario tool to start a test, then indexes the resulting run ID so you can track its progress later.

Q: How do I limit which tools the LlamaIndex agent can access?

Pass an allowedtools filter when setting up your McpToolSpec. This prevents the agent from calling destructive tools like stoptestrun while still allowing it to read metrics.

Index real-browser load testing metrics and scenario configurations into your LlamaIndex knowledge base.

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Connect LoadNinja (Real-Browser Load Testing) MCP to LlamaIndex

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Setup LoadNinja (Real-Browser Load Testing) with LlamaIndex

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Indexing LoadNinja scenario parameters for RAG

The `get_scenario` tool on this MCP Server gets the exact configurations, target URLs, and settings of your load tests. LlamaIndex takes this raw data and indexes it directly into your vector store, making your test configurations searchable. When you query your knowledge base about current test setups, the agent pulls from this index instead of guessing. You get precise answers grounded in your actual `list_scenarios` history.

Semantic search over historical performance runs

The `get_test_run_stats` tool fetches the raw performance metrics from completed runs. LlamaIndex ingests these metrics, allowing your agent to correlate historical trends across multiple test executions. Instead of digging through old reports, you ask your agent to compare recent runs. It queries `list_test_runs` to pull the right data, matches it against past indexed runs, and highlights exactly when latency started to creep up.

Grounding agent decisions in MCP Server limits

The `get_account` tool provides your LlamaIndex agent with real-time subscription details and virtual user limits. This data is indexed immediately to keep your agent informed of what resources are left. Before the agent decides to trigger `run_scenario`, it queries its own index to check the account limits. If the index shows you are running low on virtual user minutes, the agent alerts you instead of blindly starting a test.

Setup guide

Set up LoadNinja (Real-Browser Load Testing) 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 LoadNinja (Real-Browser Load Testing) 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 LoadNinja (Real-Browser Load Testing) tools.",
)
response = await agent.run("List recent LoadNinja (Real-Browser Load Testing) data")

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Common questions about LoadNinja (Real-Browser Load Testing) MCP in LlamaIndex

You'll use the `llama-index-tools-mcp` package to connect the MCP Server. Once connected, use the `McpToolSpec` to feed tool outputs from `get_test_run_stats` directly into your document indexers.

Yes, your agent calls `list_test_runs` to get a list of active runs, then indexes their current completion status to provide real-time updates through your search interface.

Yes, the agent uses the `run_scenario` tool to start a test, then indexes the resulting run ID so you can track its progress later.

Pass an `allowed_tools` filter when setting up your `McpToolSpec`. This prevents the agent from calling destructive tools like `stop_test_run` while still allowing it to read metrics.

Your account limits and scenario parameters remain isolated. The Vinkius sandbox isolates the MCP Server, keeping your credentials hidden. Only the plain-text metrics you explicitly query get written to your local vector database.

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