Can I check if a file is publicly accessible through the agent?

Yes. The `get_object_access_control` tool allows your AI agent to retrieve the Access Control List (ACL) for any specific file, helping you audit permissions and identify public objects.

How do I protect my files from accidental deletion in a bucket?

You can use the `enable_bucket_versioning` tool to activate versioning on your bucket. This ensures that historical copies of your files are preserved, allowing you to recover from accidental overwrites or deletions.

Can I see where my data is physically stored?

Absolutely. Use the `get_bucket_datacenter_location` tool to retrieve the specific geographic region where a bucket is hosted, ensuring you meet data residency requirements.

Wasabi MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Wasabi
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Wasabi through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Wasabi "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Wasabi?"
    )
    print(result.data)

asyncio.run(main())

Fully ManagedVinkius Servers

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High SecurityEnterprise-grade

IAMAccess control

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DLPData protection

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Ed25519Audit chain

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Wasabi MCP Server

Connect your Wasabi Hot Cloud Storage account to any AI agent and take full control of your cloud assets through natural conversation.

Pydantic AI validates every Wasabi tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Bucket Management — List all storage buckets, create new ones, or delete obsolete containers in your account
Object Discovery — Browse and list files (objects) stored within specific buckets, including sizes and last modified dates
Data Integrity — Enable and check bucket versioning to protect against accidental file overwrites or deletions
Access Control — Audit permissions and retrieve Access Control Lists (ACL) for specific files to ensure security
Data Residency — Verify the physical geographic region where your data is hosted for compliance needs
Cleanup Tasks — Identify fractured file uploads that consume storage and permanently delete obsolete assets

The Wasabi MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Wasabi to Pydantic AI via MCP

Follow these steps to integrate the Wasabi MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Wasabi with type-safe schemas

Why Use Pydantic AI with the Wasabi MCP Server

Pydantic AI provides unique advantages when paired with Wasabi through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Wasabi integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Wasabi connection logic from agent behavior for testable, maintainable code

Wasabi + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Wasabi MCP Server delivers measurable value.

Type-safe data pipelines: query Wasabi with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Wasabi tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Wasabi and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Wasabi responses and write comprehensive agent tests

Wasabi MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Wasabi to Pydantic AI via MCP:

create_storage_bucket

Provide a globally unique lower-kebab-case name. Creates a new high-availability storage bucket in the configured Wasabi region

delete_bucket_object

This action is irreversible. Permanently deletes a specific file from a bucket

delete_storage_bucket

Note: The bucket must be completely empty first. This action is irreversible. Permanently removes an empty storage bucket

enable_bucket_versioning

Activates object versioning for a bucket

get_bucket_datacenter_location

Retrieves the physical geographic region where a bucket is hosted

get_bucket_versioning_status

Checks if object versioning is enabled for a bucket

get_object_access_control

Retrieves the access control list (ACL) for a specific file

list_bucket_objects

Returns file keys, sizes, and last modified dates. Lists the files (objects) stored within a specific bucket

list_pending_multipart_uploads

Lists incomplete multipart uploads in a bucket

list_storage_buckets

Lists all Wasabi storage buckets visible to the authenticated IAM user

Example Prompts for Wasabi in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Wasabi immediately.

"List all my storage buckets in Wasabi."

"What files are inside the 'backups-2026' bucket?"

"Is versioning enabled for my 'user-data-prod' bucket?"

Troubleshooting Wasabi MCP Server with Pydantic AI

Common issues when connecting Wasabi to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Wasabi + Pydantic AI FAQ

Common questions about integrating Wasabi MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your Wasabi MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.