Where do I find my Personal Access Token?

In Optimizely, go to **Profile Settings** > **API Access** and generate a new Personal Access Token. Ensure it has the necessary permissions for the projects you want to manage.

Can I start an experiment that is in draft mode?

Yes, using the `start_experiment` tool will attempt to set the status to 'running'. However, Optimizely requires the experiment to be valid and fully configured before it can transition from draft to running.

How do I find my Project ID?

You can use the `list_projects` tool to see all projects associated with your account along with their IDs. You can also find it in the URL when viewing a project in the Optimizely dashboard.

Optimizely MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: Optimizely
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Optimizely through Vinkius, pass the Edge URL in the `mcps` parameter and every Optimizely tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Optimizely Specialist",
    goal="Help users interact with Optimizely effectively",
    backstory=(
        "You are an expert at leveraging Optimizely tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Optimizely "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

When paired with CrewAI, Optimizely becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Optimizely tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

Follow these steps to integrate the Optimizely MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 10 tools from Optimizely

Why Use CrewAI with the Optimizely MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Optimizely through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Optimizely + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Optimizely MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Optimizely for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Optimizely, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Optimizely tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Optimizely against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Optimizely MCP Tools for CrewAI (10)

These 10 tools become available when you connect Optimizely to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

Common issues when connecting Optimizely to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Optimizely + CrewAI FAQ

Common questions about integrating Optimizely MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.