How to Use the WHO Athena API MCP in Pydantic AI

Q: What if I need to check connectivity?

You use checkapistatus. This simple function confirms the WHO GHO Athena service is operational before your agent attempts any data retrieval calls. It keeps everything reliable.

Q: Can I list all possible dimensions using Pydantic AI?

Yes, call listhealthdimensions. The tool returns a strictly typed object, meaning you can rely on the structure of the observation dimensions when building your analysis code.

Guarantee perfect data structure for health analytics using Pydantic AI.

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Connect WHO Athena API MCP to Pydantic AI

Create your Vinkius account to connect WHO Athena API to Pydantic AI 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 Pydantic AI

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Discover indicators with guaranteed schemas.

The `list_health_indicators` tool gives your agent a list of all available codes. Since the response is validated by Pydantic, you get a predictable, type-safe structure every time. This means you don't have to write custom parsing logic for fields; the framework handles that validation for you.

Define dimensions with strict types.

Use `list_health_dimensions` when your agent needs to know what parameters are available. Because of Pydantic, if the API sends back unexpected data in this list, the entire process fails loudly. This prevents silent corruption—you’ll know instantly if the input structure is wrong.

Retrieve validated health data sets.

When you call `get_health_indicator_data`, Pydantic ensures that the returned fields match your expected Python model. You get predictable output for time series or single points. This correctness focus is huge; it lets you build complex, critical applications where failure isn't an option.

Setup guide

Set up WHO Athena API MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "who-athena-api-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to WHO Athena API tools.",
)

result = await agent.run("List recent WHO Athena API transactions")
print(result.output)

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Common questions about WHO Athena API MCP in Pydantic AI

The core function of Pydantic is validation. When your agent calls any tool, the resulting data structure must match a defined model. If it doesn't, the process halts immediately.

You use `check_api_status`. This simple function confirms the WHO GHO Athena service is operational before your agent attempts any data retrieval calls. It keeps everything reliable.

Yes, call `list_health_dimensions`. The tool returns a strictly typed object, meaning you can rely on the structure of the observation dimensions when building your analysis code.

It's rock solid. The runtime validation means that even if the external API changes its output, your agent won't crash silently—it throws a clear error.

The server touches global health indicator data. Pydantic AI ensures that the data structures handling this sensitive information are always correctly validated and typed.

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