How can I check if an experiment is currently active on a specific site?

Use the `list_experiments` tool to get a summary of all experiments, which includes their current status (e.g., 'ACTIVE', 'PAUSED') and assigned site IDs.

Can I see the defined variations for a particular experiment?

Yes, the `list_variations` tool takes an experiment ID and returns all variants configured for that test, along with their metadata.

Is it possible to list targeting rules and segments?

Absolutely. You can use the `list_segments` and `list_targeting_rules` tools to inspect how your traffic is being allocated across your experimentation ecosystem.

Kameleoon MCP Server for LangChain 10 tools — connect in under 2 minutes

Name: Kameleoon
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Kameleoon through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "kameleoon": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Kameleoon, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* 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.

LangChain's ecosystem of 500+ components combines seamlessly with Kameleoon through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

Follow these steps to integrate the Kameleoon MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Kameleoon via MCP

Why Use LangChain with the Kameleoon MCP Server

LangChain provides unique advantages when paired with Kameleoon through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Kameleoon MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Kameleoon queries for multi-turn workflows

Kameleoon + LangChain Use Cases

Practical scenarios where LangChain combined with the Kameleoon MCP Server delivers measurable value.

RAG with live data: combine Kameleoon tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Kameleoon, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Kameleoon tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Kameleoon tool call, measure latency, and optimize your agent's performance

Kameleoon MCP Tools for LangChain (10)

These 10 tools become available when you connect Kameleoon to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

Common issues when connecting Kameleoon to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Kameleoon + LangChain FAQ

Common questions about integrating Kameleoon MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.