Can I see how many users completed a specific tour?

Yes! Use the `get_experience_details` tool with the tour's unique ID. The agent will return engagement metrics including completion counts and active status.

How do I identify a user to start showing them tours?

Use the `identify_chameleon_user` tool. Provide a unique user ID and any relevant custom properties. This allows Chameleon to target that user with the appropriate segments and experiences.

Where do I find my Chameleon Account Secret?

Log in to your Chameleon Dashboard, navigate to 'Settings' in the sidebar, and then select 'API'. You can view and copy your Account Secret from there.

Chameleon.io MCP Server for CrewAI 8 tools — connect in under 2 minutes

Name: Chameleon.io
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 8 Tools Framework

Connect your CrewAI agents to Chameleon.io through Vinkius, pass the Edge URL in the `mcps` parameter and every Chameleon.io 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

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Chameleon.io Specialist",
    goal="Help users interact with Chameleon.io effectively",
    backstory=(
        "You are an expert at leveraging Chameleon.io 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 Chameleon.io "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 8 available tools "
        "and what they can do."
    ),
)

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

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* 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.

When paired with CrewAI, Chameleon.io becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Chameleon.io 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

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 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 Chameleon.io to CrewAI via MCP

Follow these steps to integrate the Chameleon.io 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 8 tools from Chameleon.io

Why Use CrewAI with the Chameleon.io MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Chameleon.io 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

Chameleon.io + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Chameleon.io 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 Chameleon.io, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Chameleon.io 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 Chameleon.io against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Chameleon.io MCP Tools for CrewAI (8)

These 8 tools become available when you connect Chameleon.io to CrewAI 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 CrewAI

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

Common issues when connecting Chameleon.io 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.

Chameleon.io + CrewAI FAQ

Common questions about integrating Chameleon.io 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.