Gatling MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Gatling as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Gatling. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Gatling?"
)
print(response)
asyncio.run(main())
* 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.
LlamaIndex agents combine Gatling tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Gatling MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
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
Why Use LlamaIndex with the Gatling MCP Server
LlamaIndex provides unique advantages when paired with Gatling through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Gatling tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Gatling tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Gatling, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Gatling tools were called, what data was returned, and how it influenced the final answer
Gatling + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Gatling MCP Server delivers measurable value.
Hybrid search: combine Gatling real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Gatling to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Gatling for fresh data
Analytical workflows: chain Gatling queries with LlamaIndex's data connectors to build multi-source analytical reports
Gatling MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Gatling to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Gatling to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpGatling + LlamaIndex FAQ
Common questions about integrating Gatling MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Gatling with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Gatling to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.