How to Use the Cloudflare MCP in CrewAI

Q: How do CrewAI agents collaborate on Cloudflare route management?

One agent can analyze active patterns using listworkerroutes and pass the data to another agent, which then runs createworkerroute to apply the changes.

Q: Can a CrewAI agent query Cloudflare D1 databases autonomously?

Yes. An agent assigned to database tasks can use queryd1 to run SQL commands, analyze the results, and share the insights with other agents in the crew.

Q: How does CrewAI secure my Cloudflare D1 database queries?

All SQL queries executed via queryd1 run through encrypted tunnels. Vinkius handles the database credentials securely, meaning raw connection strings are never exposed to the LLM or stored in the agent's memory.

Deploy a team of CrewAI agents to monitor, secure, and scale your Cloudflare edge using this MCP Server.

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Connect Cloudflare MCP to CrewAI

Create your Vinkius account to connect Cloudflare 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 Cloudflare with CrewAI

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Deploy a CrewAI agent team to manage edge routing

Let a team of specialized agents coordinate your routing. A routing analyst agent checks active routes with `list_worker_routes`, while a security agent reviews active zones with `list_zones`. Together, they decide where to deploy new endpoints. Once they agree, a deployment agent uses `create_worker_route` to apply the changes. This collaborative approach prevents routing conflicts and ensures your edge configuration matches your security policies.

Automate threat response with this collaborative MCP Server

Set up an autonomous security crew to monitor your zones. An analyst agent pulls performance data using `get_zone_analytics`, while a database agent queries security logs with `query_d1`. If they detect an attack, they escalate the issue to a mitigation agent. The mitigation agent can immediately purge caches using `purge_cache` or update Worker routes to block malicious traffic. The crew logs every action to shared memory, giving you a full audit trail.

Let specialized agents manage secrets and rotations

Automate credential management without exposing raw values to a single agent. A security auditor agent lists active secrets with `list_secrets` to find outdated keys, then instructs a deployment agent to replace them using the MCP Server. The deployment agent uses `create_secret` to inject the new API keys and `delete_secret` to remove the old ones. Because the tasks are split, no single agent has full control over your credentials.

Setup guide

Set up Cloudflare 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 Cloudflare tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

One agent can analyze active patterns using `list_worker_routes` and pass the data to another agent, which then runs `create_worker_route` to apply the changes.

Yes. An agent assigned to database tasks can use `query_d1` to run SQL commands, analyze the results, and share the insights with other agents in the crew.

CrewAI agents coordinate their tool calls using shared memory to avoid hammering the API. If a rate limit occurs, they pause and retry the action systematically.

Yes. You can use CrewAI's tool filtering to restrict sensitive operations like `delete_worker` to specific admin agents, while letting other agents use read-only tools.

All SQL queries executed via `query_d1` run through encrypted tunnels. Vinkius handles the database credentials securely, meaning raw connection strings are never exposed to the LLM or stored in the agent's memory.

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