Optimizely MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Optimizely 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 Optimizely. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Optimizely?"
)
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 Optimizely MCP Server
Connect your Optimizely account to any AI agent and take full control of your experimentation and feature management workflows through natural conversation.
LlamaIndex agents combine Optimizely tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Project Overview — List all projects and retrieve detailed metadata to maintain a clear view of your workspace.
- Experiment Management — List experiments, check current status (running, paused, draft), and retrieve detailed configurations.
- Feature Flag Tracking — List feature flags and inspect their definitions across different projects.
- Audience & Event Auditing — List defined audiences and conversion events to verify your targeting and tracking setup.
- Live Controls — Start or pause experiments directly through the agent to react quickly to results or issues.
The Optimizely MCP Server exposes 10 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 Optimizely to LlamaIndex via MCP
Follow these steps to integrate the Optimizely 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 10 tools from Optimizely
Why Use LlamaIndex with the Optimizely MCP Server
LlamaIndex provides unique advantages when paired with Optimizely through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Optimizely tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Optimizely tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Optimizely, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Optimizely tools were called, what data was returned, and how it influenced the final answer
Optimizely + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Optimizely MCP Server delivers measurable value.
Hybrid search: combine Optimizely real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Optimizely 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 Optimizely for fresh data
Analytical workflows: chain Optimizely queries with LlamaIndex's data connectors to build multi-source analytical reports
Optimizely MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Optimizely to LlamaIndex via MCP:
get_experiment
Get details for a specific experiment
get_feature_flag
Get details for a specific feature flag
get_project
Get details for a specific project
list_audiences
List defined audiences in a project
list_events
List conversion events in a project
list_experiments
List experiments in a project
list_feature_flags
List feature flags in a project
list_projects
List all Optimizely projects
pause_experiment
Set experiment status to paused
start_experiment
Set experiment status to running
Example Prompts for Optimizely in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Optimizely immediately.
"List all Optimizely projects in my account."
"Check the status of all experiments in project 12345."
"Pause experiment 67890 in project 12345."
Troubleshooting Optimizely MCP Server with LlamaIndex
Common issues when connecting Optimizely to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpOptimizely + LlamaIndex FAQ
Common questions about integrating Optimizely 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 Optimizely 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 Optimizely to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.