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

Q: Can I use CHA₂DS₂-VASc & HAS-BLED Calculator to build RAG apps with LlamaIndex?

Yes. You can combine the scores from our MCP tools, like getriskrecommendations, with vast amounts of medical literature into a single, queryable index.

LlamaIndex: Indexing CHA₂DS₂-VASc & HAS-BLED scores into a searchable knowledge base for LlamaIndex.

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

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

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Grounding API Data in Knowledge

The output from the calculator becomes part of your indexed knowledge. Instead of just calling `query_cha2ds2_vasc_score` and forgetting the result, you index it. Later, you can query that specific score against documents or other data sources to ground an answer in verifiable API facts.

Building RAG Applications

This MCP helps build sophisticated Retrieval Augmented Generation (RAG) apps. You combine the live results of `calculate_hasbled_score` with a patient's clinical notes, creating one unified index. The resulting knowledge base lets you ask questions that span both documents and live API data.

Querying Historical Risk Profiles

You can store past risk assessments in your vector store. When a new patient arrives, LlamaIndex allows you to query their record against previous scores. This helps the clinician spot trends or changes in risk that might be missed otherwise.

Setup guide

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

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all CHA₂DS₂-VASc & HAS-BLED Calculator MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to CHA₂DS₂-VASc & HAS-BLED Calculator tools.",
)
response = await agent.run("List recent CHA₂DS₂-VASc & HAS-BLED Calculator data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by CHA₂DS₂-VASc & HAS-BLED Calculator. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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

LlamaIndex takes the structured output from our calculator—both stroke and bleeding scores—and embeds them. This makes the data searchable, allowing you to reference specific risk calculations in your knowledge base.

Yes. You can combine the scores from our MCP tools, like `get_risk_recommendations`, with vast amounts of medical literature into a single, queryable index.

It touches clinical risk scores and associated patient profile details. This raw score data is what gets indexed into your vector store for later retrieval.

You can save multiple runs of our tools and query them together. This allows your application to retrieve data points from various assessments for comparison.

Yes, by indexing the results into a vector store, the calculation outputs become permanent, searchable parts of your application's knowledge base.

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