How to Use the WHO Athena API MCP in LlamaIndex

Build searchable knowledge bases with LlamaIndex and WHO Athena API.

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Connect WHO Athena API MCP to LlamaIndex

Create your Vinkius account to connect WHO Athena API 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 WHO Athena API with LlamaIndex

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Indexing Global Health Data for LlamaIndex

Instead of just calling tools, you index the results from the MCP Server into a vector store. When your user asks a complex question about historical trends, LlamaIndex searches its knowledge base first. This means that API data—like the output from `get_health_indicator_data`—becomes searchable context. You get answers grounded in actual WHO records, not just generalized text.

Listing Indicators and Dimensions via LlamaIndex

Use `list_health_indicators` or `list_health_dimensions` to populate your initial index content. The developer can run these calls once and store the full list of available metrics and dimensions. Later, when a query comes in, LlamaIndex searches this indexed metadata first, helping guide the agent toward the correct API call parameters.

Checking WHO Athena API Status with LlamaIndex

Your RAG application needs to know if its data source is reliable. You can incorporate `check_api_status` into your startup sequence and index that status check result. This ensures that the knowledge base itself contains operational awareness, allowing the agent to refuse answering or warning the user when the WHO GHO service goes down.

Setup guide

Set up WHO Athena API 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 WHO Athena API 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 WHO Athena API tools.",
)
response = await agent.run("List recent WHO Athena API 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 WHO Athena. 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 WHO Athena API MCP in LlamaIndex

LlamaIndex takes structured data retrieved from the MCP Server and turns it into semantic chunks. This allows you to query historical trends or configuration details using natural language against the raw WHO health indicator data.

You build a searchable index based on global health records. For example, you could query past sessions to find out what indicators were analyzed last month, or ask 'what are the available country codes?'

Yes. By indexing both the metadata (indicators and dimensions) and the raw results (`get_health_indicator_data`), you combine live API access with persistent, queryable knowledge.

The biggest win is permanence. Direct calls are transient; LlamaIndex makes them permanent. The API output becomes part of a unified index, accessible long after the initial query completes.

The server touches global health indicator codes and related time-series data. Since this is public domain metadata, it’s structured reporting rather than private patient records.

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