How to Use the Blood Type Compatibility & Demographics MCP in LangChain

Q: How is the pregnancy risk data protected in LangChain?

Data for analyzepregnancyrisk stays in your local environment during the chain execution.

Chain complex hematology logic into your LangChain agents.

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Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Blood Type Compatibility & Demographics MCP to LangChain

Create your Vinkius account to connect Blood Type Compatibility & Demographics to LangChain — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

GDPR Included with Plan

Setup Blood Type Compatibility & Demographics with LangChain

Ask AI about this MCP

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Gemini

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Clinical Safety Logic

Use `get_rbc_compatibility` to check if a donor can safely provide red blood cells. Your agent gets a direct answer based on standard markers. This MCP handles the heavy lifting of matching donor types to recipients. You don't have to hardcode every rule.

Logistics and Supply Planning

Get regional data with `get_population_distribution`. This helps you see where blood types are most common in specific areas. Your agent can use this to suggest where to prioritize blood drives. It turns raw numbers into actionable plans.

Patient Risk Assessment

Combine `analyze_pregnancy_risk` and `get_plasma_compatibility` to assess transfusion safety and Rh issues. You can build a multi-step chain that checks both conditions before giving a final answer. This MCP lets your agent handle complex, multi-part queries. It keeps the logic consistent across every step of the chain.

Setup guide

Set up Blood Type Compatibility & Demographics 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 Blood Type Compatibility & Demographics 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({
    "blood-type-compatibility-demographics-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 Blood Type Compatibility & Demographics transactions"
    })
    print(result["messages"][-1].content)

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