How to Use the Wasabi MCP in CrewAI

Q: How does Wasabi handle data residency when using CrewAI?

You can call getbucketdatacenterlocation to know the physical region of a bucket. This allows your crew to design operations that respect geographical boundaries.

Q: What happens if I try to delete files in Wasabi with CrewAI?

The deletebucketobject tool permanently removes the file, and this action is irreversible. Your workflow must account for this finality.

Q: Can a multi-agent system monitor Wasabi status using CrewAI?

Yes. An agent can check getbucketversioningstatus to confirm if versioning is enabled, giving the whole crew accurate information about data protection.

Q: How does the MCP Server track incomplete uploads for Wasabi with CrewAI?

The listpendingmultipartuploads tool helps your autonomous operations discover and report on any unfinished, large-scale file transfers in a bucket.

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Monitor and list Wasabi resources with MCP Server

A specialized agent can use `list_storage_buckets` to report all buckets visible across the account. This is useful for a monitor agent checking resource availability. Furthermore, agents can check which files exist in any bucket using `list_bucket_objects`, returning keys and modification dates.

Manage Wasabi object life cycle with CrewAI

If an object needs to go, the agent uses `delete_bucket_object` for permanent removal. Since this action is irreversible, a moderator agent must validate the request. To clear out old resources entirely, use `delete_storage_bucket`. Remember that empty buckets are required before this final step.

Get location and permissions details with CrewAI

Need to know where your data resides? The `get_bucket_datacenter_location` tool retrieves the physical region. This helps specialized agents plan cross-regional operations. The team also uses `get_object_access_control` to confirm that the necessary permissions are attached before any action is taken.

Setup guide

Set up Wasabi 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 Wasabi tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

You can call `get_bucket_datacenter_location` to know the physical region of a bucket. This allows your crew to design operations that respect geographical boundaries.

The `delete_bucket_object` tool permanently removes the file, and this action is irreversible. Your workflow must account for this finality.

Yes. An agent can check `get_bucket_versioning_status` to confirm if versioning is enabled, giving the whole crew accurate information about data protection.

The `list_pending_multipart_uploads` tool helps your autonomous operations discover and report on any unfinished, large-scale file transfers in a bucket.

This Wasabi MCP Server deals with object keys, file sizes, and access control lists (ACLs). The entire metadata set is available for inspection by your agents.

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