How to Use the Wasabi MCP in AutoGen

Q: How do I list my buckets using Wasabi and AutoGen?

You give the agent permission to run liststoragebuckets. The resulting list is fed into the multi-agent system, allowing agents to discuss which buckets are missing or need renaming.

Q: Can AutoGen check object versions with Wasabi?

Yes. An agent can run getbucketversioningstatus and report that status back to the group. This provides necessary context for agents debating whether versioning should be enabled or disabled.

Q: Does Wasabi and AutoGen help with temporary uploads?

You can use listpendingmultipartuploads. This tool gives the agent a list of incomplete data transfers, which the agents then debate whether they should be cleared out or kept for debugging.

Q: How do I get detailed object access control with Wasabi and AutoGen?

Run getobjectaccesscontrol. This retrieves the specific ACL. The agent group then debates whether this access level is appropriate given the file's sensitivity, achieving consensus on security.

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Setup Wasabi with AutoGen

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Determine Optimal Bucket Structure

You can set up a multi-agent conversation where agents argue over the best bucket strategy. One agent calls `list_storage_buckets` to gather all available resources, while another agent uses `create_storage_bucket` to propose the optimal new structure based on the gathered data. The debate converges on a decision—the exact naming convention and purpose of the new storage resource.

Audit Data Residency Requirements

Agents can simulate compliance checks. One agent calls `get_bucket_datacenter_location` to confirm where data resides, while a second agent verifies this against regulatory rules you provide. They debate whether the current location satisfies all requirements. This consensus-driven process validates infrastructure setup before any changes are committed.

Manage Data Retention Policies

The agents can negotiate data cleanup protocols. One agent might read file details using `list_bucket_objects`, and another suggests a deletion approach, perhaps calling `enable_bucket_versioning` first to ensure recovery paths exist before the eventual irreversible deletion. It's a dialogue that moves from discovery to policy implementation.

Setup guide

Set up Wasabi MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Wasabi tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Wasabi_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Wasabi data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Wasabi_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Wasabi data")
    print(result.messages[-1].content)

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Common questions about Wasabi MCP in AutoGen

You give the agent permission to run `list_storage_buckets`. The resulting list is fed into the multi-agent system, allowing agents to discuss which buckets are missing or need renaming.

Yes. An agent can run `get_bucket_versioning_status` and report that status back to the group. This provides necessary context for agents debating whether versioning should be enabled or disabled.

The server touches metadata about storage objects, including file keys, sizes, and last modified dates. These details allow agents to argue whether a file is stale or if its deletion is safe.

You can use `list_pending_multipart_uploads`. This tool gives the agent a list of incomplete data transfers, which the agents then debate whether they should be cleared out or kept for debugging.

Run `get_object_access_control`. This retrieves the specific ACL. The agent group then debates whether this access level is appropriate given the file's sensitivity, achieving consensus on security.

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