How to Use the CHA₂DS₂-VASc & HAS-BLED Calculator MCP in Pydantic AI

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Connect CHA₂DS₂-VASc & HAS-BLED Calculator MCP to Pydantic AI

Create your Vinkius account to connect CHA₂DS₂-VASc & HAS-BLED Calculator 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 CHA₂DS₂-VASc & HAS-BLED Calculator with Pydantic AI

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Validated Stroke Risk Scores

The `query_cha2ds2_vasc_score` tool calculates the CHA₂DS₂-VASc score, giving you a definitive number for stroke risk. Because this MCP Server response is validated against Pydantic models, you're never going to get corrupted or hallucinated fields. It guarantees that the output structure matches exactly what your agent expects, making it rock solid for production pipelines.

Structured Bleeding Risk Assessment

When you run `calculate_hasbled_score`, the returned bleeding risk data is always type-safe. This means if the API returns unexpected data, your agent fails LOUDLY with a validation error—you know immediately that something went wrong. This prevents silent corruption in mission-critical clinical workflows.

Reliable Decision Output

You can get final advice by calling `get_risk_recommendations`. The MCP Server takes the calculated scores and structures them into a reliable, predictable output that your agent can trust. It’s structured data you can build on. This approach is model-agnostic; it works whether you're running an OpenAI or Anthropic backend.

Setup guide

Set up CHA₂DS₂-VASc & HAS-BLED Calculator 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": {
        "cha2ds2-vasc-has-bled-calculator-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to CHA₂DS₂-VASc & HAS-BLED Calculator tools.",
)

result = await agent.run("List recent CHA₂DS₂-VASc & HAS-BLED Calculator transactions")
print(result.output)

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Common questions about CHA₂DS₂-VASc & HAS-BLED Calculator MCP in Pydantic AI

The MCP Server provides structured data that is validated by Pydantic models at runtime. This means you get guaranteed, correct outputs for both risk scores and recommendations every single time.

Yes. Because every response must pass through a validation layer, the data integrity is extremely high. You won't have to worry about unexpected fields or bad formats breaking your agent.

The MCP Server ensures zero-trust security for all credentials. They pass through a proxy, are used only in transit, and never saved to disk.

Yep. The tool is model-agnostic. As long as your agent client can connect via HTTP, it will correctly call and process all three scoring tools.

The toolset handles structured patient demographics and history flags needed to calculate both stroke risk using `query_cha2ds2_vasc_score` and bleeding risk with `calculate_hasbled_score`.

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