How to Use the Wasabi MCP in Google ADK

Q: Can the Google ADK manage multiple Wasabi buckets?

Yes. It uses liststoragebuckets to show all accessible containers, allowing your agent to switch context between different data sources seamlessly. This makes multi-source reasoning much simpler.

Q: Does Wasabi support versioning with the Google ADK?

Yes. The agent can activate object versioning using enablebucketversioning. This critical feature ensures that when your agents modify files, old versions are saved and accessible later.

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Connect Wasabi MCP to Google ADK

Create your Vinkius account to connect Wasabi to Google ADK 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 Wasabi with Google ADK

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Perplexity

Manage data storage for Gemini models.

The `list_storage_buckets` tool lets your agent see every Wasabi bucket visible to the connected IAM user. Before running any complex query, your agent can get a full manifest of available data containers. For deep context reasoning, your agent first uses `get_bucket_datacenter_location`. Knowing where the data physically lives helps Gemini optimize its long-context retrieval.

Ensure data integrity and safety.

When dealing with sensitive information in BigQuery or Vertex AI, your agent needs to confirm permissions. It uses `get_object_access_control` to check the specific file's ACL before processing it. This prevents unauthorized actions. If you need a full inventory of files, running `list_bucket_objects` returns keys and modification dates for every object in a bucket.

Control data lifecycle efficiently.

Need to clean out old resources? The `delete_storage_bucket` tool permanently removes an empty storage bucket. Your agent handles this removal, ensuring no stale infrastructure costs are incurred. If you're working with large files and need to confirm they aren't corrupted, the agent can list incomplete uploads via `list_pending_multipart_uploads`. This is key for reliable data pipelines.

Setup guide

Set up Wasabi MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Wasabi tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Wasabi_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Wasabi tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Wasabi MCP in Google ADK

The MCP Server integrates directly, letting your Gemini-powered agents access cloud storage tools. Your agent can execute bucket and object management commands using the native toolsets within the Google ADK framework.

Yes. It uses `list_storage_buckets` to show all accessible containers, allowing your agent to switch context between different data sources seamlessly. This makes multi-source reasoning much simpler.

The system requires confirmation for destructive actions like `delete_bucket_object`. The agent ensures you know exactly what is being permanently removed before it executes any command.

Yes. The agent can activate object versioning using `enable_bucket_versioning`. This critical feature ensures that when your agents modify files, old versions are saved and accessible later.

This server manages objects (files) and associated bucket metadata. It allows your agent to track file keys, sizes, last modified dates, and perform versioning checks on those objects.

Use it with your favorite AI tools

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