How to Use the Chameleon.io MCP in AutoGen

Q: How do I configure Chameleon.io inside an AutoGen setup?

Use the mcpservertools function with StreamableHttpServerParams pointing to your endpoint. Pass the resulting tool list directly into your AssistantAgent constructor.

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Connect Chameleon.io MCP to AutoGen

Create your Vinkius account to connect Chameleon.io to AutoGen 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 AutoGen

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Perplexity

Debate Chameleon.io MCP Server strategies

Product decisions require competing perspectives. You assign one agent to advocate for aggressive onboarding while another defends user experience. They pull live metrics using `list_experiences` to ground their arguments. The performance agent might suggest a new tour, but the UX agent checks `list_chameleon_events` and points out high drop-off rates on existing prompts. They negotiate a compromise before executing any changes.

Autonomous user lifecycle management

Managing user identity is tedious work. You hand this off to a dedicated data agent that monitors your internal database and compares it against `list_user_segments`. When a user upgrades their account, a separate security agent verifies the permission change. Once they reach consensus, the data agent fires `identify_chameleon_user` to update the profile. The system maintains perfect sync without human intervention.

Resolve negative microsurvey feedback

Bad feedback requires immediate action. You build a swarm where a primary analyst agent constantly polls `list_microsurvey_responses` looking for low scores. When it finds an angry response, it alerts a remediation agent. That agent uses `get_experience_details` to figure out which feature caused the issue, then triggers `track_user_event` to flag the account for manual review. The agents handle the entire triage process.

Setup guide

Set up Chameleon.io MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Chameleon.io tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Chameleon.io_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Chameleon.io data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Chameleon.io_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Chameleon.io data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Chameleon. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Chameleon.io MCP in AutoGen

Use the mcp_server_tools function with StreamableHttpServerParams pointing to your endpoint. Pass the resulting tool list directly into your AssistantAgent constructor.

They share the same tool registry. You can give the survey tool to a research agent while restricting the user deletion endpoint to an admin agent.

The McpToolAdapter converts the server's OpenAPI definitions into native JSON schemas. The agents know exactly what parameters to pass without custom prompting.

Yes, if you expose that specific endpoint. You should configure a human-in-the-loop approval step before an agent executes the deletion command.

We handle authentication via a single endpoint token. The raw microsurvey text and user identity payloads process inside a V8 Isolate sandbox that guarantees zero cross-tenant data leakage.

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