Show HN: I just built a MCP Server that connects Claude to all your wearables

3•anton_salcher•1h ago

Hey HN,

I built Pace, a Claude Connector that lets you connect all your wearables (Garmin, Polar, Whoop, 20+) with Claude.

You connect your devices once and can analyze your data with Claude. No Dashboard needed, just in natural language. I already use it everyday, especially the visualization tool in Claude makes this really cool to use.

Tools include: overview, sleep, training, activity, samples and trends.

Tech Stack: Python (FastMCP), Google Cloud Run, Google Cloud SQL (PostgreSQL) and Firebase

It is live and free to try (no Claude Pro/MAX Plan needed)..

Would love feedback, especially from people who've built MCP servers or use wearables seriously.

Comments

anton_salcher•1h ago

I was a former professional athlete and built this mainly for myself. I wanted to analyze my training in Claude. When I first tried it, it was amazing. So I wanted to build it for everyone. So I created a small Dashboard and a OAuth flow and now everyone can try it.

KaiLetov•1h ago

Nice, I've been messing around with MCP servers lately too. One thing I ran into, Garmin's Connect API has pretty tight rate limits, something like 25 requests per 15 minutes if I remember right. Did you hit that? Also wondering if you're storing raw data in Postgres or just aggregated stuff. Because with sleep tracking you get a datapoint every 30 seconds, that adds up fast.

anton_salcher•1h ago

Thanks, yes I also saw the Garmin MCP and I also tried the TrainingsPeaks MCP. I have a Polar Watch so I needed to build something for myself. I use Terra API for my Data Pipeline. So they normalize and aggregate my Data (so Terra handles all provider-specific rate limits). Storage is a mix, every workout, sleep or daily record gets its own with row extracted summary fields as typed columns (HR, HRV or duration) plus the raw time-series stored as JSONB arrays.

Your point about sleep tracking is real but the JSONB arrays compress well in Postgres and a night's worth of 30s data is 1-2K data points, so it's manageable. The bigger concern is the query performance when you need 90d of sleep data. what MCP Servers have you tried out, something similar?

thitami•1h ago

The JSONB approach for time-series is pragmatic for this scale. The 90-day sleep query concern is real though — have you considered a partial index on the timestamp field within the JSONB, or is the aggregation layer from Terra making that unnecessary? Also curious about the MCP server design: are you streaming responses back to Claude or returning complete payloads? For trend analysis over 90 days that could be a meaningful difference in perceived latency.

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Comments

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