How to Use the WHO Athena API MCP in AutoGen

Get consensus on WHO data analysis with AutoGen's multi-agent debate framework.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect WHO Athena API MCP to AutoGen

Create your Vinkius account to connect WHO Athena API to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup WHO Athena API with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Debating Data Strategy using AutoGen

Don't just run a query; have agents argue over the best way to structure it. You can set up an agent pair: one specialized in WHO data retrieval and another focused on methodological risk assessment. They debate which indicators are most critical. The MCP Server provides the tools for them to gather facts. The consensus reached is the final, vetted answer—a major step up from a single AI call.

Managing WHO Athena API Tools with AutoGen

AutoGen allows you to give multiple agents access to the same pool of tools (`list_health_indicators`, `get_health_indicator_data`). The agents then challenge each other on tool usage. One agent might suggest listing dimensions, while another argues it needs to check the API status first. This forces a discussion that leads to a more robust plan for data access.

Consensus-Driven Health Reporting with AutoGen

You can build an autonomous system where agents collaborate on finding trends. One agent might call `list_health_indicators` to get the list, and a second agent then drafts multiple reports using that data. The conversation structure forces cross-checking of results, improving accuracy before any final output is generated.

Setup guide

Set up WHO Athena API MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes WHO Athena API tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="WHO Athena API_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent WHO Athena API data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="WHO Athena API_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent WHO Athena API data")
    print(result.messages[-1].content)

Get your connection token →

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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect WHO Athena API now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about WHO Athena API MCP in AutoGen

AutoGen passes the MCP Server tools to its agents. Instead of one agent running a linear script, multiple specialized agents debate which tool—like `get_health_indicator_data`—is necessary and what parameters it should use.

Absolutely. This is its strength. If one agent pulls a metric that another agent flags as potentially outdated (by checking the service status via `check_api_status`), they debate it until they agree on the final, most trustworthy conclusion.

The system handles global health indicators, observation dimensions (country, year), and the resulting quantitative time-series data. The agents talk about these structured records.

For tasks where the 'right' answer requires deliberation—like determining if three separate indicators are correlated—AutoGen is superior. It simulates human teamwork, which is often necessary when analyzing complex data.

The server touches global health indicator codes and associated public trend data. The system’s focus is on structured, aggregated epidemiological metrics.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript