How to Use the Leonardo.ai (Generative AI & Models) MCP in Pydantic AI

Q: Can I run image-to-image pipelines with Leonardo.ai (Generative AI & Models) in Pydantic AI?

Yes, you can. The agent executes uploadinitimage to fetch a typed presigned URL object, uploads the asset, and passes the validated reference to the generation tool.

Q: How do I handle fine-tuned models safely in Pydantic AI?

Your agent queries listcustommodels, parses the list into a structured Pydantic model, and enforces that only valid, active custom model IDs are used for subsequent generations.

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Connect Leonardo.ai (Generative AI & Models) MCP to Pydantic AI

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Setup Leonardo.ai (Generative AI & Models) with Pydantic AI

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Type-safe image generation loops in Pydantic AI

Ensure your Leonardo.ai generation parameters match strict schemas before hitting the API. When your Pydantic AI agent calls `generate_image`, the framework validates the prompt, aspect ratio, and model parameters at runtime, catching errors before they consume API credits. This runtime validation guarantees that the Leonardo.ai generation IDs returned by `get_generation` conform exactly to your Python type hints, preventing silent workflow failures.

Validate model configurations before running inference

Avoid runtime crashes in Pydantic AI caused by deprecated or missing Leonardo.ai models. It's a massive pain when a production run dies because of a bad model ID. Your agent uses `get_model` to inspect model details, validating the returned configuration against a Pydantic schema before committing to a generation run. By integrating this MCP Server, you ensure that every custom Leonardo.ai model fetched via `list_custom_models` matches your Pydantic AI application's expected inputs and capabilities.

Manage user asset history with strict type safety

Track and clean up Leonardo.ai user assets without worrying about malformed API payloads in Pydantic AI. The agent lists historical runs using `list_user_generations` and processes the data through typed models to safely identify which records to target with `delete_generation`. This keeps your database records and Leonardo.ai workspace perfectly in sync, so you don't have to worry about broken state.

Setup guide

Set up Leonardo.ai (Generative AI & Models) 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": {
        "leonardoai-generative-ai-models-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Leonardo.ai (Generative AI & Models) tools.",
)

result = await agent.run("List recent Leonardo.ai (Generative AI & Models) transactions")
print(result.output)

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Common questions about Leonardo.ai (Generative AI & Models) MCP in Pydantic AI

Use the unified toolset class pointing to your Vinkius HTTP endpoint. Pass this toolset directly to your agent, which will automatically extract and validate the schemas for tools like `generate_image`.

Yes. Every tool response, including complex image arrays from `get_generation`, is validated against strict Pydantic models, raising immediate errors if the API payload changes.

Yes, you can. The agent executes `upload_init_image` to fetch a typed presigned URL object, uploads the asset, and passes the validated reference to the generation tool.

Your agent queries `list_custom_models`, parses the list into a structured Pydantic model, and enforces that only valid, active custom model IDs are used for subsequent generations.

Your API keys are encrypted at rest using industry-standard AES-256 and injected only at runtime. The validation layer ensures that actual keys never leak into your application logs or error outputs.

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