How to Use the Design Prover MCP in Pydantic AI

Q: Can I use Design Prover to validate raw CSS classes in Pydantic AI?

Yes, the provedesign tool analyzes the layout constraints, spacing, and typographic scales the agent plans to use. It blocks the agent from using generic utility classes that produce cookie-cutter designs.

Force type-safe layout validation in Pydantic AI with strict design constraints that block ugly templates.

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Connect Design Prover MCP to Pydantic AI

Create your Vinkius account to connect Design Prover 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 Design Prover with Pydantic AI

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Validate layouts in Pydantic AI with strict design rules

AI models default to the same boring layouts: hero, three cards, and a blue button. This MCP Server breaks that cycle by forcing your agent to run `prove_design` and prove its layout is unique before writing any code. Because this runs inside Pydantic AI, the layout parameters are validated against strict types at runtime. If the agent tries to return a flat design with uniform padding, the tool rejects it instantly.

Fail loudly when agents generate generic UI components

You do not want silent layout failures or boring designs slipping into production. When using this MCP tool to validate design parameters, the system checks for asymmetric columns, dramatic whitespace, and bold typographic scales. If the model tries to cheat with uniform grids, your Pydantic AI agent raises a validation error immediately. This forces the model to self-correct and generate a better layout before finalizing the output.

Validate typography and color scales at runtime

This tool analyzes specific CSS values, font pairings, and color contrast ratios. The agent must justify why it chose specific hex values and prove its typographic scale has real visual drama. By integrating this check into your agent's toolset, you ensure every generated page has a distinct visual identity. You get clean, validated UI data that matches your design constraints every time.

Setup guide

Set up Design Prover 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": {
        "design-prover-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Design Prover MCP in Pydantic AI

Use the unified MCP Server toolset class initialized with your Vinkius HTTP endpoint. Pass the toolset instance directly into your agent's constructor to expose the validation tools.

Yes, the `prove_design` tool analyzes the layout constraints, spacing, and typographic scales the agent plans to use. It blocks the agent from using generic utility classes that produce cookie-cutter designs.

When validation fails, the tool returns a detailed error explaining which design rule was violated. Your agent catches this error and uses the feedback to adjust its layout parameters.

Yes, the server integrates with the async engine of your Python framework. This ensures that layout validation does not block other agent tasks or network requests in your production app.

The tool only inspects structural layout metadata, typography ratios, and color palettes. Your database schemas, private API keys, and application code remain completely secure inside your local runtime environment and are never exposed to the external MCP Server.

Use it with your favorite AI tools

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