How to Use the Countly MCP in CrewAI

Q: Can I restrict which Countly tools my CrewAI agents can access?

Yes, you can use a toolfilter in your MCP server configuration. This lets you restrict access to sensitive tools like updateuserdetails while keeping read tools open.

Deploy specialized agent teams using CrewAI to analyze and manage your Countly product metrics autonomously.

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Connect Countly MCP to CrewAI

Create your Vinkius account to connect Countly to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Countly with CrewAI

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Role-based product analytics with CrewAI

Instead of a single model doing everything, you can split the work. One agent can monitor user sessions with `read_metrics` while a different analyst agent runs `read_drill` to find trends. These agents share memory and pass context back and forth. You get a cohesive team that detects anomalies and flags them without human intervention.

Manage sessions autonomously using an MCP Server

Let your autonomous agents handle the lifecycle of test users or simulated traffic. Your MCP agent crew can execute `begin_session`, `update_session`, and `end_session` sequentially to mimic real-world usage. This is perfect for load testing or running automated QA on your analytics pipeline. The agents coordinate their actions to ensure everything is tracked properly.

Update user details based on crew analysis

When your analyst agent spots a high-value user pattern, it can instruct the manager agent to act. The team can run `update_user_details` to tag that user with a new cohort label. This closes the loop between data analysis and user segmentation. Your agents handle the entire classification process in the background.

Setup guide

Set up Countly MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Countly tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Countly Analyst",
    goal="Access and analyze Countly data via MCP.",
    backstory="Expert analyst with direct Countly access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Countly transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

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

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Countly Analyst",
        goal="Access and analyze Countly data via MCP.",
        backstory="Expert analyst with direct Countly access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Countly transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Countly. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Countly MCP in CrewAI

The easiest way is to pass the Vinkius HTTP URL directly into the agent's mcps array. For advanced setups, use MCPServerHTTP from the crewai.mcp package.

Yes, you can use a tool_filter in your MCP server configuration. This lets you restrict access to sensitive tools like update_user_details while keeping read tools open.

Your crew can run in sequential or hierarchical mode. For instance, a researcher agent runs read_events, passes the raw metrics to an analyst agent, and the analyst compiles the final report.

Yes, you can use stdio transport for local development or SSE for production. Vinkius hosts the MCP backend so your agents can connect from anywhere.

All user details and session data are routed through an isolated V8 sandbox. Vinkius acts as a secure, ephemeral pass-through, so your telemetry is never stored on our servers.

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