Chameleon.io MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Chameleon.io 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 Chameleon.io. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Chameleon.io?"
)
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 Chameleon.io MCP Server
Connect your Chameleon.io account to any AI agent and take full control of your user onboarding and product adoption experiences through natural conversation. Streamline how you guide and engage your users.
LlamaIndex agents combine Chameleon.io tool responses with indexed documents for comprehensive, grounded answers. Connect 8 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
- Experience Oversight — List and retrieve details for all Chameleon tours, launchers, and microsurveys natively
- User Segmentation — Access and monitor your configured user segments to understand targeting flawlessly
- Response Auditing — Retrieve and analyze recent microsurvey responses to gather user feedback securely
- User Intelligence — Identify and update user profiles with custom properties in real-time
- Behavioral Tracking — Log and monitor custom user events to trigger the right experience at the right time flawlessly
- Compliance Management — Handle data deletion requests by removing user records directly within your workspace
The Chameleon.io MCP Server exposes 8 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 Chameleon.io to LlamaIndex via MCP
Follow these steps to integrate the Chameleon.io 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 8 tools from Chameleon.io
Why Use LlamaIndex with the Chameleon.io MCP Server
LlamaIndex provides unique advantages when paired with Chameleon.io through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Chameleon.io tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Chameleon.io tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Chameleon.io, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Chameleon.io tools were called, what data was returned, and how it influenced the final answer
Chameleon.io + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Chameleon.io MCP Server delivers measurable value.
Hybrid search: combine Chameleon.io real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Chameleon.io 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 Chameleon.io for fresh data
Analytical workflows: chain Chameleon.io queries with LlamaIndex's data connectors to build multi-source analytical reports
Chameleon.io MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Chameleon.io to LlamaIndex via MCP:
delete_chameleon_user
Permanently delete a user and their data from Chameleon
get_experience_details
Get details for a specific experience
identify_chameleon_user
Identify or update a user in Chameleon
list_chameleon_events
List recent events tracked by Chameleon
list_experiences
List all Chameleon experiences (Tours, Launchers, Microsurveys)
list_microsurvey_responses
List recent responses to microsurveys
list_user_segments
List all configured user segments
track_user_event
Track a custom event for a specific user
Example Prompts for Chameleon.io in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Chameleon.io immediately.
"List all my active Chameleon experiences."
"Identify user 'user_999' with plan: 'enterprise' and industry: 'fintech'."
"Track a 'checkout_completed' event for user 'user_123'."
Troubleshooting Chameleon.io MCP Server with LlamaIndex
Common issues when connecting Chameleon.io to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpChameleon.io + LlamaIndex FAQ
Common questions about integrating Chameleon.io 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 Chameleon.io 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 Chameleon.io to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.