How to Use the Google Cloud Storage MCP in Pydantic AI

Type-safe Google Cloud Storage operations for your Pydantic AI agent.

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Connect Google Cloud Storage MCP to Pydantic AI

Create your Vinkius account to connect Google Cloud Storage to Pydantic AI 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 Google Cloud Storage with Pydantic AI

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Verified storage interactions

Every response from `list_objects` or `get_object_metadata` undergoes strict validation. Pydantic AI confirms the data matches your expected schemas before the agent processes it. If the cloud returns malformed data, the agent stops immediately. This prevents silent failures during your storage audit routines.

Type-safe file management

Your agent uses `upload_object` and `copy_object` with validated parameters. Pydantic ensures your file paths and bucket names are formatted correctly before the request hits the API. When performing cleanup with `delete_object`, the agent validates the operation status. It confirms successful removal so your state remains consistent.

Reliable permission auditing

Audit your bucket security using `get_bucket_iam` and `list_bucket_acl`. Pydantic AI enforces consistent model structure for these policy documents. It checks project-level access via `get_project_service_account` and lists HMAC keys with `list_hmac_keys`. This rigorous validation keeps your security audits accurate and predictable.

Setup guide

Set up Google Cloud Storage 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": {
        "google-cloud-storage-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Google Cloud Storage transactions")
print(result.output)

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Common questions about Google Cloud Storage MCP in Pydantic AI

The MCPToolset parses the server response into a Pydantic model. If the data structure deviates from the expected schema, the agent raises a validation error.

Yes. Since Pydantic AI is model-agnostic, you can pair this server with any local or hosted model while maintaining strict type safety.

Install the slim MCP package and initialize an MCPToolset with your server endpoint. Pass this to your Agent to begin type-safe storage management.

The agent validates the structure of IAM policies returned by `get_bucket_iam`. This ensures you are reading the policy data correctly for your security checks.

The server processes transient data only. It does not cache your bucket contents or IAM policies, ensuring your private storage metadata remains secure.

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