How to Use the Cloudinary MCP in Pydantic AI

Get type-safe Cloudinary tool responses in your Pydantic AI agent.

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Connect Cloudinary MCP to Pydantic AI

Create your Vinkius account to connect Cloudinary 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 Cloudinary with Pydantic AI

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Type-safe Cloudinary tools for Pydantic AI

Every tool response is validated against Pydantic models at runtime. If `get_media_resource_details` returns malformed data, your agent stops immediately. You stop worrying about silent failures. Your code logic relies on strictly typed objects that reflect the actual state of your media library.

Validate Cloudinary tags in Pydantic AI

Use `list_media_tags` to fetch your library taxonomy. The agent verifies these tags against your predefined models before applying them to new uploads. This enforces consistency across your assets. You catch schema mismatches before they propagate into your production database.

Safe resource deletion with Pydantic AI

Wrap `delete_media_resource` in your agent's control flow. Since the output is typed, your agent confirms the deletion success before proceeding to the next step. You build robust cleanup scripts that fail loudly if something goes wrong. No more guessing if your resources were actually removed.

Setup guide

Set up Cloudinary 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": {
        "cloudinary-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Cloudinary MCP in Pydantic AI

The MCP server provides the schema. Pydantic AI automatically validates the tool output against your models at runtime.

Yes, you use the MCPToolset class to link the server. It handles the communication over SSE or HTTP without extra boilerplate.

If the API returns unexpected fields, Pydantic AI raises a validation error. You handle this in your standard try-except blocks.

It is model-agnostic. Whether you use OpenAI or a local model, the Pydantic validation ensures the data remains consistent.

All communication is encrypted in transit. The server operates in an ephemeral sandbox, ensuring your media list data is cleared after every session.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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