How to Use the Chameleon.io MCP in OpenAI Agents SDK

Manage Chameleon.io product tours and user events directly within your OpenAI Agents SDK production pipeline.

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Connect Chameleon.io MCP to OpenAI Agents SDK

Create your Vinkius account to connect Chameleon.io to OpenAI Agents SDK 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 Chameleon.io with OpenAI Agents SDK

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Full lifecycle user management in OpenAI Agents SDK

Call `identify_chameleon_user` to sync your agent's current context with your product data. This keeps your user records current without manual database updates. Trigger `delete_chameleon_user` when your agent determines a user account needs removal. It cleans up data in one step.

Analyze product adoption through OpenAI Agents SDK

Fetch current user engagement data using `list_experiences`. Your agent checks which tours or launchers are active for specific users. Use `list_microsurvey_responses` to pull raw feedback. Your agent processes these results to generate summaries for your internal reports.

Track custom behaviors via this MCP Server

Fire `track_user_event` whenever your agent detects a specific action in your application. This feeds your analytics engine with high-fidelity behavioral data. Query `list_chameleon_events` to verify the ingestion status. Your agent confirms that events reached the platform as expected.

Setup guide

Set up Chameleon.io MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Chameleon.io tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Chameleon.io tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Chameleon.io tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Chameleon.io Agent",
            instructions="You have access to Chameleon.io tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Chameleon.io MCP in OpenAI Agents SDK

Install the necessary dependencies and pass the server URL into your Agent constructor. The SDK automatically discovers all available tools.

Yes, by querying user segments or survey responses. Your agent reads these outputs to make informed decisions about user journeys.

The server functions alongside your existing safety constraints. Your agent validates tool inputs before sending data to the platform.

No. The SDK handles discovery, so you can call functions directly in your code without writing boilerplate adapters.

All user identifiers and event data remain ephemeral during the agent session. Your production system dictates exactly what information reaches our servers.

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