How to Use the DevCycle MCP in CrewAI

Q: How do I filter tools for my CrewAI agents using DevCycle?

Use the toolfilter option when defining your Agent. This limits the agent to only the DevCycle tools you want it to access.

Coordinate autonomous feature releases in CrewAI using the DevCycle MCP Server.

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Connect DevCycle MCP to CrewAI

Create your Vinkius account to connect DevCycle 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 DevCycle with CrewAI

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Specialized CrewAI agents for flag audits

Assign a dedicated monitor agent to keep your flags in check. Using `list_active_flags`, this agent constantly scans for anomalies in your current feature set. When it finds an issue, it passes the data to a moderator agent. This agent then calls `get_feature_flag_details` to determine if a rollback is necessary based on the current configuration.

Cross-project flag management

Deploy a research agent to aggregate data from multiple workspaces. It uses `list_devcycle_projects` to map out your infrastructure before diving into specific project flags. This gives your CrewAI team a full view of your release maturity. The agents work in sequence to ensure every flag is accounted for across all environments.

Automated status updates in CrewAI

Let your crew handle the routine work of updating feature status. After a successful test run, the action agent calls `update_feature_flag_status` to promote features to production. This removes the need for manual intervention during deployments. The agents verify the status change by checking `list_feature_variables` to ensure the correct values are being served.

Setup guide

Set up DevCycle 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 DevCycle tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent DevCycle 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="DevCycle Analyst",
        goal="Access and analyze DevCycle data via MCP.",
        backstory="Expert analyst with direct DevCycle access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent DevCycle 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 DevCycle. 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 DevCycle MCP in CrewAI

Use the tool_filter option when defining your Agent. This limits the agent to only the DevCycle tools you want it to access.

They can. You can configure a crew where one agent manages flags while another monitors project environments simultaneously.

Just pass the URL to your agent's mcp parameter. For advanced configurations, use the provided HTTP transport classes to connect your crew.

Data is processed ephemeral-style. We do not log your project identifiers or flag metadata during the agent's collaborative sessions.

The server uses secure transport for all communication. Your feature flag states and targeting rules are never written to disk by the MCP host.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

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