How to Use the Wasabi MCP in Pydantic AI

Q: Can Pydantic AI manage bucket creation with Wasabi?

Yes. The createstoragebucket tool allows the agent to create new buckets while enforcing structural rules on the required input parameters. This makes your agent reliable in production code.

Q: Does Pydantic AI validate object deletions using Wasabi?

Yes, validation is key here. When an agent calls deletebucketobject, the surrounding framework ensures that all inputs and expected confirmations are met before executing the irreversible deletion.

Q: Does Wasabi support versioning checks using the Pydantic AI framework?

Absolutely. The agent can use getbucketversioningstatus to check object versioning, and the returned status is typed and validated by Pydantic models for immediate programmatic use.

Type-safe storage actions for Pydantic AI Agents.

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Validate bucket creation parameters.

The `create_storage_bucket` tool lets your agent create a new Wasabi storage bucket, requiring a globally unique lower-kebab-case name. Because of Pydantic's validation, the agent fails loudly if the naming convention is wrong. It also validates that the bucket can be accessed by checking its location using `get_bucket_datacenter_location`. This guarantees your deployment environment matches expectations.

Perform controlled object listing.

The agent uses `list_bucket_objects` to get file keys, sizes, and last modified dates. Pydantic ensures this list is structured correctly for downstream processing in your application code. If you need a broader view, the `list_storage_buckets` tool provides all visible bucket names, validating the input structure before listing.

Manage object lifecycle and versions.

The system handles permanent removal with `delete_bucket_object` or `delete_storage_bucket`. Since Pydantic validates every step, you know exactly when the agent attempts these irreversible actions. It also helps validate versioning status using `get_bucket_versioning_status`, confirming if object history is active on a bucket.

Setup guide

Set up Wasabi MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "wasabi-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Wasabi tools.",
)

result = await agent.run("List recent Wasabi transactions")
print(result.output)

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Common questions about Wasabi MCP in Pydantic AI

The MCP Server exposes its tools, and the Pydantic framework validates every single response. This means when your agent calls a Wasabi tool, the returned data is guaranteed to match expected Python models.

Yes. The `create_storage_bucket` tool allows the agent to create new buckets while enforcing structural rules on the required input parameters. This makes your agent reliable in production code.

Yes, validation is key here. When an agent calls `delete_bucket_object`, the surrounding framework ensures that all inputs and expected confirmations are met before executing the irreversible deletion.

This server manages object storage infrastructure, specifically dealing with file (object) keys, metadata, and associated bucket information. Validation covers these structural elements.

Absolutely. The agent can use `get_bucket_versioning_status` to check object versioning, and the returned status is typed and validated by Pydantic models for immediate programmatic use.

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