How to Use the Health XML Export Parser MCP in AutoGen

Q: How do AutoGen agents interact with the Health XML Export Parser?

You register the server using the mcpservertools adapter. Your agents can then call parsehealthexport during their group chats to resolve data queries.

Q: Can multiple AutoGen agents use the Health XML Export Parser at once?

Yes, any agent in the conversation can trigger the parsehealthexport tool. They can share the resulting metrics to debate fitness trends.

Let your AutoGen agents debate and analyze massive Apple Health XML exports using this local MCP setup.

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Connect Health XML Export Parser MCP to AutoGen

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Setup Health XML Export Parser with AutoGen

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Analyze fitness metrics using AutoGen and this MCP Server

Run deep fitness analyses by letting your AutoGen agents debate the data. One agent runs `parse_health_export` while another analyzes the metrics. The agents can discuss if your heart rate matches your active energy burned. This debate produces better insights than a single run.

Feed structured XML data to your agent conversation

Massive health XMLs will break an AutoGen chat if you pass them raw. This server solves that by streaming the fitness data into summaries. Your AutoGen agents receive a clean JSON summary from `parse_health_export`. This keeps your multi-agent conversation history light.

Connect the MCP Server to AutoGen agents instantly

The McpToolAdapter handles all schema conversions for AutoGen automatically. Your agents call `parse_health_export` using standard JSON payloads. This lets you drop the MCP Server into any AutoGen group chat. The agents will use the fitness data whenever a health question comes up.

Setup guide

Set up Health XML Export Parser 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 Health XML Export Parser 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="Health XML Export Parser_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Health XML Export Parser data")
print(result.messages[-1].content)

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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="Health XML Export Parser_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Health XML Export Parser data")
    print(result.messages[-1].content)

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Common questions about Health XML Export Parser MCP in AutoGen

You register the server using the mcp_server_tools adapter. Your agents can then call `parse_health_export` during their group chats to resolve data queries.

Yes, any agent in the conversation can trigger the `parse_health_export` tool. They can share the resulting metrics to debate fitness trends.

It aggregates millions of XML rows into structured metrics. This ensures your agents only discuss the summaries, keeping the MCP chat history clean.

You can use either stdio or the Streamable HTTP transport. Both options let your agents call `parse_health_export` with minimal latency.

The server runs entirely inside a local V8 sandbox. Your private heart rate, sleep, and step count data never leave your local environment during execution.

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