How to Use the UPYUN Developer Platform MCP in CrewAI

Q: How do agents discover UPYUN directory contents using UPYN Developer Platform and CrewAI?

The research agent calls listdirectory via the MCP Server. This function provides a comprehensive view of all folders, allowing specialized roles to target their work precisely.

Q: Can CrewAI manage file creation with UPYUN Developer Platform?

Yes. The createtextfile tool allows an acting agent to generate and upload new content to the cloud, which is immediately visible in the shared memory for other agents.

Q: Does UPYN Developer Platform support resource auditing with CrewAI?

It does. By calling getserviceusage and getfileinfo, your monitor agent can maintain a real-time audit log of the cloud infrastructure's health and asset properties.

Autonomous cloud operations using CrewAI agents with UPYUN Developer Platform.

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Connect UPYUN Developer Platform MCP to CrewAI

Create your Vinkius account to connect UPYUN Developer Platform 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 UPYUN Developer Platform with CrewAI

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Agent Discovery of Cloud Assets

The research agent can locate required assets by calling `list_directory`. This gives the crew a full map of what's in an UPYUN bucket, allowing specialized agents to know exactly where to look for data. It’s how you make sure that Agent B doesn't waste time searching when Agent A already found the relevant folder.

Autonomous Data Modification via MCP Server

The acting agent executes changes using `create_text_file` or `delete_file`. If a role requires new documentation, it generates and uploads it. If old data is stale, it removes it. This makes the crew truly autonomous. The shared memory knows that these file actions happened, ensuring subsequent agents don't repeat work.

Auditing Cloud State with CrewAI

The monitor agent keeps track of resource health by calling `get_service_usage` and checking individual assets with `get_file_info`. This audit trail is critical for high-stakes operations. The crew can automatically check if the current file count or quota exceeds a threshold before attempting any major actions.

Setup guide

Set up UPYUN Developer Platform 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 UPYUN Developer Platform tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

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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="UPYUN Developer Platform Analyst",
        goal="Access and analyze UPYUN Developer Platform data via MCP.",
        backstory="Expert analyst with direct UPYUN Developer Platform access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent UPYUN Developer Platform 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 UPYUN. 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 UPYUN Developer Platform MCP in CrewAI

The research agent calls `list_directory` via the MCP Server. This function provides a comprehensive view of all folders, allowing specialized roles to target their work precisely.

Yes. The `create_text_file` tool allows an acting agent to generate and upload new content to the cloud, which is immediately visible in the shared memory for other agents.

It does. By calling `get_service_usage` and `get_file_info`, your monitor agent can maintain a real-time audit log of the cloud infrastructure's health and asset properties.

This server handles file metadata (properties, size), directory structures, and service usage metrics. It deals with structured information about cloud resources.

It's designed for multi-agent collaboration, meaning the tools support complex, sequential actions that modify and verify resource state in a controlled manner.

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