How to Use the Health XML Export Parser MCP in Pydantic AI

Q: How do I set up the Health XML Export Parser and Pydantic AI?

Install pydantic-ai-slimmcp. Initialize an MCP toolset with your HTTP endpoint URL, and pass it into the toolsets array when creating your Agent.

Q: Can I filter which health metrics the Health XML Export Parser extracts?

Currently, the parsehealthexport tool automatically aggregates standard actionable metrics like heart rate and step count to prevent context overflow.

Type-safe health data extraction for Pydantic AI. Parse massive XML exports with guaranteed output schemas.

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Connect Health XML Export Parser MCP to Pydantic AI

Create your Vinkius account to connect Health XML Export Parser 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 Health XML Export Parser with Pydantic AI

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Strict Validation for Pydantic AI

The `parse_health_export` tool streams raw XML health exports and aggregates the data for your Pydantic AI agent. When it hands the results back, the framework validates every single metric against your predefined Pydantic models. If the tool returns a string where a float should be, the agent fails loudly. You get a clear validation error immediately. There is no silent data corruption and no hallucinated health fields bleeding into your database.

Model-Agnostic Parsing via MCP

The `parse_health_export` tool operates identically regardless of which LLM you plug into Pydantic AI. Because this is an MCP Server, the parsing logic is decoupled from the model. You might use OpenAI for initial extraction and a local model for sensitive analysis. The agent simply provides the absolute file path to the tool. The server handles the heavy lifting of iterating through gigabytes of XML, returning clean JSON that any model can interpret.

Streaming Large Files Safely

The `parse_health_export` tool prevents Python out-of-memory errors by using a streaming SAX parser. Loading a 2GB Apple Health export into memory will kill your Python process. This tool solves that by reading XML elements sequentially. It extracts step counts, heart rates, and active energy burned on the fly. Your agent receives condensed, actionable metrics that fit comfortably within standard context windows, preventing token overflow.

Setup guide

Set up Health XML Export Parser 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": {
        "health-xml-export-parser-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Health XML Export Parser tools.",
)

result = await agent.run("List recent Health XML Export Parser transactions")
print(result.output)

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Common questions about Health XML Export Parser MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]`. Initialize an MCP toolset with your HTTP endpoint URL, and pass it into the `toolsets` array when creating your Agent.

Yes. Pydantic AI is model-agnostic. As long as your local model supports function calling, it can trigger the `parse_health_export` tool.

The streaming parser handles minor inconsistencies gracefully. If the file is completely unreadable, the tool returns an explicit error that Pydantic AI will catch and report.

Currently, the `parse_health_export` tool automatically aggregates standard actionable metrics like heart rate and step count to prevent context overflow.

Vinkius routes your Apple Health and Google Fit XML data through a zero-trust, ephemeral MCP sandbox. Authentication requires a single endpoint token, and the temporary environment is destroyed immediately after parsing.

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