Kameleoon 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 Kameleoon 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 Kameleoon. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Kameleoon?"
)
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 Kameleoon MCP Server
Empower your AI agents to control your Kameleoon experimentation platform. This MCP server enables seamless management of experiments, variations, and audience segments directly from natural language interfaces.
LlamaIndex agents combine Kameleoon 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
- Experiment Control — List all active experiments and drill down into specific configurations and metadata
- Variation Management — Inspect A/B variations and their statuses across different digital properties
- Site Inventory — Query all sites and properties registered in your account to ensure correct environment targeting
- Audience Segmentation — List defined audience segments and targeting rules used for precise traffic allocation
- Results Triggering — Request latest results reports to analyze experiment performance on the fly
The Kameleoon 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 Kameleoon to LlamaIndex via MCP
Follow these steps to integrate the Kameleoon 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 Kameleoon
Why Use LlamaIndex with the Kameleoon MCP Server
LlamaIndex provides unique advantages when paired with Kameleoon through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Kameleoon tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Kameleoon tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Kameleoon, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Kameleoon tools were called, what data was returned, and how it influenced the final answer
Kameleoon + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Kameleoon MCP Server delivers measurable value.
Hybrid search: combine Kameleoon real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Kameleoon 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 Kameleoon for fresh data
Analytical workflows: chain Kameleoon queries with LlamaIndex's data connectors to build multi-source analytical reports
Kameleoon MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Kameleoon to LlamaIndex via MCP:
create_experiment
Requires a name and a site ID. Create a new experiment
get_experiment
Get details for a specific experiment
get_experiment_results
This is an asynchronous process in the Kameleoon API. Request a results report for an experiment
get_site
Get details for a specific site
list_custom_data
List custom data dimensions
list_experiments
Use this to monitor campaign statuses and identify active experiments. List all experiments in Kameleoon
list_segments
List audience segments
list_sites
List all sites in the account
list_targeting_rules
List targeting rules
list_variations
) associated with a specific experiment ID. List variations for an experiment
Example Prompts for Kameleoon in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Kameleoon immediately.
"Show me all active experiments in my Kameleoon account."
"What are the variations for experiment ID '12345'?"
"List all sites registered in my Kameleoon profile."
Troubleshooting Kameleoon MCP Server with LlamaIndex
Common issues when connecting Kameleoon to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpKameleoon + LlamaIndex FAQ
Common questions about integrating Kameleoon 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 Kameleoon 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 Kameleoon to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.