fast-xml-parser Connector for AI agents.
1 live capability
Analyze years of fitness history from large health exports without crashing your chat.
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Why people use fast-xml-parser
Health XML Export Parser for Analyzing Massive Fitness Data
This Connector changes that by acting as a filter. It reads the massive file locally and gives your AI client a condensed summary. You go from struggling with a file too large error to asking specific questions about your sleep or heart rate in seconds.
What Vinkius changes
You get to query years of health data without hitting context limits.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Analyzing sleep trends
A longevity researcher wants to see sleep patterns over five years.
- Real-world use case 02
Auditing heart rate data
A fitness enthusiast wants to audit their heart rate data.
- Real-world use case 03
Identifying wearable sources
A tech hobbyist wants to see which wearable sent the most data.
Complete set · 1capability
The complete fast-xml-parser capability set.
These are the exact actions your AI can choose when you ask it to work with fast-xml-parser.
01
1 capability in this set.
Part of 1 available through fast-xml-parser.
- 01 Capability
Parse health export
Provide a local file path to your export.xml for the Connector to scan. It summarizes your health metrics so your AI client can read them without crashing.
Set up in minutes
One URL. Then ask fast-xml-parser to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use fast-xml-parser from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_izt8YYlP2e8qd3u4pL8fA9ekld5jdITBzS2X1sY3/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it fast-xml-parser, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable fast-xml-parser for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_izt8YYlP2e8qd3u4pL8fA9ekld5jdITBzS2X1sY3/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the fast-xml-parser URL.
- Step 03
Save and start
Save the connection and enable fast-xml-parser in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"health-xml-export-parser": {
"url": "https://edge.vinkius.com/vk_preview_izt8YYlP2e8qd3u4pL8fA9ekld5jdITBzS2X1sY3/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using fast-xml-parser
Open Agent mode in chat and ask: "Using fast-xml-parser, help me...". 1 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"health-xml-export-parser": {
"url": "https://edge.vinkius.com/vk_preview_izt8YYlP2e8qd3u4pL8fA9ekld5jdITBzS2X1sY3/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using fast-xml-parser
Ask Copilot: "Using fast-xml-parser, help me...". 1 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"health-xml-export-parser": {
"url": "https://edge.vinkius.com/vk_preview_izt8YYlP2e8qd3u4pL8fA9ekld5jdITBzS2X1sY3/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using fast-xml-parser
Open Cascade and ask: "Using fast-xml-parser, help me...". 1 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"health-xml-export-parser": {
"url": "https://edge.vinkius.com/vk_preview_izt8YYlP2e8qd3u4pL8fA9ekld5jdITBzS2X1sY3/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using fast-xml-parser
Ask Cline: "Using fast-xml-parser, help me...". 1 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add health-xml-export-parser --transport http "https://edge.vinkius.com/vk_preview_izt8YYlP2e8qd3u4pL8fA9ekld5jdITBzS2X1sY3/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using fast-xml-parser
Ask Claude: "Using fast-xml-parser, show me...". 1 tools are ready
Where the request belongs
Work fast-xml-parser can move forward.
For the health enthusiast who has years of data trapped in a massive XML file and wants to find actual patterns without manually sorting spreadsheets.
Longevity Researcher
Analyzes years of heart rate and sleep data to find correlations and long-term trends.
Fitness Tracker
Audits device sources and step counts across different wearables to ensure data accuracy.
Data Hobbyist
Wants to turn a messy export into a clean summary for personal health records and archives.
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Bring your own AI
Change the model, client or framework. Keep fast-xml-parser connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
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Cline -
Zed -
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Before you connect
Questions about fast-xml-parser.
The practical details behind the request, access and result.
Can the Health XML Export Parser handle files larger than 100MB?
Yes, it is built to handle multi-gigabyte files that would normally crash an AI chat by aggregating the data locally.
Is my health data sent to any cloud service?
No, this Connector parses your files locally on your machine to ensure your data stays private.
What types of health data can I analyze?
You can analyze anything in your Apple Health or Google Fit export, like heart rate, steps, and sleep.
Does the Health XML Export Parser work with Google Fit?
Yes, it works with both Apple Health and Google Fit XML exports.
How do I use this to see my sleep patterns?
You ask your agent to summarize your sleep records, and it will use the Connector to give you a clear overview of your history.
Will this crash my AI client?
No, the Connector aggregates the data first so your AI client only ever sees a small summary of the records.
Will it send my raw heart rate data to the AI?
No, to protect your privacy and token limit, it only sends a summary of what data exists (the schema) and a tiny sample (the first 50 records) to Claude. The rest never leaves your computer.
Can it process a 1GB Apple Health export file?
Yes, the XML parser is highly optimized. However, parsing massive 1GB XML files requires sufficient available RAM on your local machine.
Does it work with Google Fit?
Yes! If Google Fit data is exported as an XML structure, this engine will parse and summarize its root nodes perfectly.
One connection away
Give your agent a direct line to fast-xml-parser.
Connect fast-xml-parser once. Keep it beside 6,100+ managed Connectors when the next task needs more.
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