Introducing the Optimizely MCP Server for Experimentation

At Optimizely, we're always looking for ways to make experimentation more seamless, intuitive, and integrated into your daily workflow. Today, we're thrilled to announce a significant leap forward for developers: the Optimizely MCP Server for Experimentation.

This isn't just another integration; it's a fundamental shift in how you'll interact with your experimentation program. Imagine managing your feature flags and experiment lifecycle entirely from your Integrated Development Environment (IDE), using natural language. That's exactly what the Optimizely MCP Server for Experimentation lets you do. Say goodbye to constant context switching between your code and dashboards.

What is MCP?

You might be wondering, "What's MCP?" MCP stands for Model Context Protocol. It's an open standard that lets AI products talk to external tools and services. Think of it as a universal translator between your AI-powered IDE (like Cursor, Claude Code, or other MCP-enabled tools) and the platforms you use every day. Instead of building custom integrations for every AI tool, MCP gives us a common language. This means any compatible AI assistant can communicate with any MCP-enabled service.

Our MCP Server uses this protocol to give AI products native access to our experimentation platform.So, your AI coding assistant doesn't just help you write code; it can now create feature flags, configure experiments, and analyze your experimentation program as naturally as it helps you debug a function or refactor a component. It’s about making AI a true experimentation collaborator.

Key capabilities: Development-integrated experimentation

The Optimizely MCP Server brings the full power of Web and Feature Experimentation directly to your development environment. Here’s a peek at what you can do:

1. Manage experimentation from your existing tools

Create and configure feature flags and experiments without ever leaving your IDE. Just tell it what you need in plain language:

• "Create a feature flag called pricing_redesign with control and treatment variations"
• "Set up an A/B test for the checkout flow with 3 variations, tracking conversion on purchase_completed"
• "Enable the new_onboarding flag for 20% of users in staging"

What used to take multiple API calls—creating flags, setting up variables, configuring environments, establishing audiences, and setting rollout rules—now happens with a single command. The MCP Server handles all the orchestration, making sure everything is created in the right order with proper dependencies.

2. Debug implementation issues instantly

When things go sideways, you need answers fast. The MCP Server helps diagnose problems in real-time:

• "Why is the homepage_experiment flag returning false in development?"
• "What experiment contains the custom JavaScript that's throwing 'undefined is not a function' errors?"
• "Show me the targeting conditions for the checkout_flow experiment"

Get immediate insights into flag evaluation, targeting rules, and configuration issues without digging through multiple screens. It's about getting you back to building, faster.

3. Automate flag lifecycle management

Old feature flags can pile up, creating technical debt. The MCP Server makes cleanup simple:

• "Which flags in my codebase haven’t been updated in a while?”
• "Show me unused flags in the authentication service codebase"
• "Generate a cleanup plan for stale flags in production"

The server scans your codebase, cross-references with Optimizely, and gives you actionable recommendations for keeping your experimentation setup clean and tidy.

4. Generate SDK integration code

Stop copying boilerplate code from documentation. Get production-ready code instantly:

• "Generate React SDK integration for the recommendation_engine flag"
• "Create TypeScript definitions for all active feature flags"
• "Show me how to implement the checkout_flow flag with proper error handling"

The generated code includes error handling, fallback values, and follows best practices for your specific framework. It’s all about making your life easier.

How it works: Smart, secure, seamless

The Optimizely MCP Server runs locally on your machine, connecting your AI-powered development tools directly to Optimizely's APIs. Here’s why it’s a game-changer:

1. Local execution for instant performance: The MCP Server works entirely within your development environment with a local database for caching and state management. This means zero network latency for common operations and instant responses during your development flow.
2. Comprehensive tool coverage: It supports both Web Experimentation and Feature Experimentation in a single integration.
3. Intelligent abstraction: Complex, multi-step operations become simple, single commands. The server handles all the API orchestration, dependency management, and error handling that would normally need careful coordination.
4. Secure by design: It uses your existing Optimizely API credentials and runs entirely on your local machine. No data is sent to external servers. Your data stays where it belongs.
5. Context-aware: It understands your project structure and can make intelligent suggestions based on your codebase.

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What’s next: The future of AI-driven experimentation

As MCP adoption grows across the development ecosystem, we’re excited about the possibilities ahead. We’re envisioning a future where AI agents can:

1. Orchestrate complex marketing workflows: Imagine autonomous campaign management agents creating dozens of interconnected personalization campaigns, managing cascading audiences, and updating priorities across your entire program.
2. Suggest proactive optimizations: AI agents could analyze your code and suggest where feature flags could reduce risk, before you even think to ask.
3. Deliver intelligent experiment analysis: Get natural language queries about experiment performance and automated insights, helping you make data-driven decisions faster.
4. Connect your entire martech stack: An extensible plugin architecture will let you extend the MCP Server’s capabilities, syncing audience segments from Optimizely Data Platform and automatically creating targeted experiments.

Join the beta: Be part of the future

Ready to ditch context switching and supercharge your experimentation workflow?

The Optimizely MCP Server for Experimentation is currently in closed beta. Sign up at optimizely.com/beta to request access.

Beta participants will get:

• Early access to the MCP Server
• Direct support from our product team
• The opportunity to shape the future of AI-driven experimentation

We can’t wait to see what you build with it!