How do I find my GrowingIO Project ID and Client ID?

Log in to your GrowingIO portal, go to [Project Configuration] -> [Project Info]. You will find your Project ID (ai) and Client ID (Public Key) there.

Where do I generate the API Token?

In your GrowingIO portal, navigate to [Project Configuration] -> [API Token Management] to generate a long-term API Token for this server.

Can I query quantitative metrics through the agent?

Yes. Use the `get_metrics` tool with a JSON query definition. This allows your agent to retrieve specific data points like DAU, retention rates, or custom conversion metrics.

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

Name: GrowingIO
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to GrowingIO through Vinkius, pass the Edge URL in the `mcps` parameter and every GrowingIO 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="GrowingIO Specialist",
    goal="Help users interact with GrowingIO effectively",
    backstory=(
        "You are an expert at leveraging GrowingIO 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 GrowingIO "
        "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 GrowingIO MCP Server

Empower your AI agent to orchestrate your product analytics and user behavioral data with GrowingIO, the premier analytical platform in China. By connecting GrowingIO to your agent, you transform complex event tracking, user segmentation, and metric analysis into a natural conversation. Your agent can instantly list tracked events, retrieve detailed user segment metadata, monitor conversion funnels, and execute quantitative metric queries without you ever needing to navigate the comprehensive GrowingIO web interface. Whether you are conducting a product health audit or monitoring real-time campaign performance, your agent acts as a real-time data analyst assistant, keeping your product data accurate and your growth moving.

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

Event Orchestration — List and retrieve detailed metadata for all tracked behavioral events in your project.
User Segmentation — Browse and monitor user segments to identify high-value cohorts and behavioral patterns.
Metric Querying — Execute quantitative queries to retrieve specific performance metrics via natural language.
Funnel Auditing — Retrieve detailed configuration and data for conversion funnels to identify drop-off points.
Campaign Insights — Browse tracked advertising campaigns and identify successful growth drivers.

The GrowingIO 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 GrowingIO to CrewAI via MCP

Follow these steps to integrate the GrowingIO 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 GrowingIO

Why Use CrewAI with the GrowingIO MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with GrowingIO 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

GrowingIO + CrewAI Use Cases

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

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

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

GrowingIO MCP Tools for CrewAI (10)

These 10 tools become available when you connect GrowingIO to CrewAI via MCP:

get_event

Get event details

get_funnel

Get conversion funnel details

get_metrics

Query project metrics

get_project_info

Get project metadata

get_segment_users

Get users in a segment

list_ads

List advertising campaigns

list_events

List project events

list_log_sources

). List data log sources

list_segments

List user segments

list_variables

List tracked variables

Example Prompts for GrowingIO in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with GrowingIO immediately.

"List all user segments in GrowingIO."

"Show me the conversion funnel for 'Checkout Flow'."

"Query the DAU for the last 7 days."

Troubleshooting GrowingIO MCP Server with CrewAI

Common issues when connecting GrowingIO 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.

GrowingIO + CrewAI FAQ

Common questions about integrating GrowingIO 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.