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

LangChain: Build multi-step clinical reasoning chains using our CHA₂DS₂-VASc & HAS-BLED Calculator.

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Connect CHA₂DS₂-VASc & HAS-BLED Calculator MCP to LangChain

Create your Vinkius account to connect CHA₂DS₂-VASc & HAS-BLED Calculator to LangChain 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 LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build Decision Pipelines

Your agent can run full assessment pipelines. It first calls `query_cha2ds2_vasc_score` to establish the stroke risk profile, then runs `calculate_hasbled_score` for bleeding concerns. The chain doesn't stop there. The next step is calling `get_risk_recommendations`, which synthesizes both scores into a single, actionable clinical summary.

Orchestrate Complex Workflows

The LangChain framework lets the agent dynamically decide the sequence of calls. If preliminary results are flagged, it can loop back to adjust inputs or gather more context. This means you're not just running tools; you're building a self-correcting reasoning flow across multiple steps.

Full Observability for Agents

You get full visibility into every step the agent takes. LangSmith tracing shows exactly which inputs led to which outputs, down to the tool call level. This granular view helps you debug complex clinical logic and understand why the final recommendation was made.

Setup guide

Set up CHA₂DS₂-VASc & HAS-BLED Calculator MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes CHA₂DS₂-VASc & HAS-BLED Calculator tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "cha2ds2-vasc-has-bled-calculator-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent CHA₂DS₂-VASc & HAS-BLED Calculator transactions"
    })
    print(result["messages"][-1].content)

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

LangChain lets your agent use our calculator to build a decision chain. The agent can sequence calls like checking stroke risk, then bleeding risk, and finally compiling the recommendation.

Yes. You can integrate this MCP into a persistent context within your agent's session, allowing you to reference previous risk assessments for continuity of care.

It handles clinical scores and patient history details required for calculating both the CHA₂DS₂-VASc score and the HAS-BLED score. This is structured, quantitative risk data.

Absolutely. The core strength here is that your agent can combine multiple MCP tools—like calling the stroke score and then the bleeding score—to create a robust, sequential analysis.

You can build a comparison pipeline. Your agent calls the necessary tools and then structures the resulting data side-by-side for easy clinical review.

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