How to Use the Chameleon.io MCP in LlamaIndex

Index product tours and microsurvey responses directly into your LlamaIndex RAG applications.

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Connect Chameleon.io MCP to LlamaIndex

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

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Claude

Perplexity

Index your product experiences

Support bots usually guess how onboarding works. Now you can pull the exact configuration using `list_experiences` and dump the output straight into your vector store. Your bot knows exactly what the user saw. When a customer asks about a specific feature, the query engine retrieves the relevant tour info via `get_experience_details`. The AI formulates an answer grounded in your actual product setup, completely eliminating hallucinations about UI elements.

Query Chameleon.io MCP Server feedback

User feedback dies in dashboards. You fix that by routing the output of `list_microsurvey_responses` into a dedicated document index. Product managers just ask questions in plain English. The RAG pipeline searches the indexed survey data to find out why users hate the new dashboard. It synthesizes hundreds of individual text responses into a single, accurate summary.

Build behavior-aware search

Context matters when answering support tickets. By wrapping `list_chameleon_events` in an MCP tool spec, your system checks what the user clicked right before they submitted the ticket. The client combines this event timeline with your documentation index. If the logs show they just completed a specific segment, verified by `list_user_segments`, the generated response skips the basic tutorial and jumps straight to advanced troubleshooting.

Setup guide

Set up Chameleon.io MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Chameleon.io MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Chameleon.io tools.",
)
response = await agent.run("List recent Chameleon.io data")

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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 LlamaIndex

Install llama-index-tools-mcp via pip. Initialize a BasicMCPClient, wrap it in an McpToolSpec, and call to_tool_list_async() to expose the endpoints to your function agent.

You have full control over the exposed endpoints. Use the allowed_tools filter parameter to restrict your RAG application to just read-only operations like checking survey data.

The agent fetches the current configurations using the segment listing tool. It then embeds that JSON output into your vector store, making the grouping logic fully searchable.

The toolkit handles asynchronous execution natively. This keeps your RAG pipeline fast when fetching large batches of recent product events.

The Vinkius zero-trust environment isolates your server instance completely. Segment configurations and individual event logs pass through an ephemeral container that wipes memory instantly upon task completion.

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