How to Use the Chi-Square Test Engine MCP in Pydantic AI

Inject type-safe categorical analysis into your Pydantic AI agents with this deterministic MCP Server.

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Connect Chi-Square Test Engine MCP to Pydantic AI

Create your Vinkius account to connect Chi-Square Test Engine 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 Chi-Square Test Engine with Pydantic AI

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Type-safe statistical tests for Pydantic AI

The `calculate_chi_square` tool ensures that your categorical analysis pipeline never suffers from silent data corruption. In Pydantic AI, every input and output is validated against strict schemas at runtime, meaning your agent cannot pass malformed matrices to the engine. If the agent attempts to pass non-integer counts or uneven rows, the system raises a validation error immediately. This strict typing guarantees that your statistical runs are clean and predictable.

Model-agnostic statistical execution

The `calculate_chi_square` tool works regardless of which LLM powers your Pydantic AI agent. Whether you are running local open-source models or commercial APIs, the math remains identical because it executes on our dedicated CPU cores. This makes your agent code highly portable. You can swap models on the fly without worrying about how a different neural network handles complex decimal division or chi-square distributions.

Fail-fast architecture for production MCP pipelines

The `calculate_chi_square` tool is built to fail loudly and clearly when encountering statistical anomalies, such as empty tables or zero variances. Pydantic AI catches these errors instantly when running the MCP tool, allowing your agent to handle the exception gracefully. This prevents your production application from hanging or returning garbage data to your users. You get clean, predictable execution blocks that fit perfectly into your existing test suites.

Setup guide

Set up Chi-Square Test Engine 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": {
        "chi-square-test-engine-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Chi-Square Test Engine transactions")
print(result.output)

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Common questions about Chi-Square Test Engine MCP in Pydantic AI

Use the MCPToolset class pointing to the Vinkius HTTP endpoint and add it to your agent's toolsets list. Avoid the deprecated HTTP server classes to ensure compatibility.

The tool schema is converted into a Pydantic model at runtime. This forces your agent to validate the contingency table structure before sending the API request.

Yes, since the engine is model-agnostic, you can use local models like Llama or Mistral. The agent still gets access to the exact same CPU-backed math tool.

It supports both Streamable HTTP and SSE transports. This allows you to connect to externally hosted servers with minimal latency.

The server processes your categorical contingency tables in an ephemeral, isolated environment with zero data retention. Your categorical counts are processed in memory and immediately purged once the statistical calculation finishes.

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