How to Use the Color Contrast Checker MCP in Pydantic AI

Enforce type-safe WCAG contrast checks in your Pydantic AI pipelines with strict runtime validation.

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Connect Color Contrast Checker MCP to Pydantic AI

Create your Vinkius account to connect Color Contrast Checker 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 Color Contrast Checker with Pydantic AI

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Validate contrast ratios in Pydantic AI

`check_color_contrast` returns the exact mathematical ratio between two colors while enforcing strict runtime type checks. Your Pydantic AI agent uses this tool to ensure color inputs match expected hex formats. If the server returns malformed data, your agent fails loudly immediately. This prevents bad contrast calculations from corrupting your UI generation pipeline.

Check error text contrast with this MCP Server

`check_danger_text_contrast` verifies that red error text remains readable on your application's background colors. Your Pydantic AI agent calls this tool to ensure critical UI alerts meet accessibility standards. The tool validates the contrast ratio against WCAG AA requirements. Your agent uses this output to dynamically adjust the background if the danger text is unreadable.

Verify text against pure white and black surfaces

`check_white_text_on_bg` and `check_black_text_on_bg` calculate contrast ratios for pure white or black text on custom background colors. Your Pydantic AI agent runs these tools to validate minimalist UI designs. You register these tools using the MCP Server over a secure SSE or HTTP transport. The agent parses the returned contrast ratios into structured Pydantic models for immediate validation.

Setup guide

Set up Color Contrast Checker 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": {
        "color-contrast-checker-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Color Contrast Checker transactions")
print(result.output)

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Common questions about Color Contrast Checker MCP in Pydantic AI

Use the MCP Server constructor with the Vinkius HTTP endpoint and pass it to your agent via the toolsets argument. Do not use the deprecated HTTP classes.

Yes, every contrast ratio returned by the MCP Server is parsed into structured Pydantic models. If the server returns an invalid structure, your agent raises a validation error immediately.

Yes, because Pydantic AI is model-agnostic. You can run these accessibility checks using local models, OpenAI, or Anthropic.

It checks for WCAG AA and AAA compliance levels. The tools output precise numeric ratios so you can enforce strict thresholds.

The MCP Server only receives basic hex codes and background/foreground RGB values. It runs inside an ephemeral, zero-trust V8 isolate sandbox, ensuring your application's design variables are never cached or logged.

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