Server Details
Pace is a remote MCP server that exposes wearable and fitness data to Claude via the Model Context Protocol. It connects to Garmin, Oura, Whoop, Polar, Fitbit and 20+ devices and provides 15 tools for querying sleep, activity, recovery, and training data. Hosted on Google Cloud Run, OAuth 2.1 authentication, Streamable HTTP transport.
Instructions: First you need to create an account at: https://pacetraining.co and connect your wearables. After that you can connect the remote Server via Custom Connector in Claude and OAuth 2.1 Flow startet.
- Status
- Unhealthy
- OAuth
- Not checked
- Last Tested
- Transport
- Streamable HTTP
- URL
Frequently Asked Questions
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io.github.alice/server, link the matching GitHub user or an account that owns the GitHub organization, then choose Claim with GitHub.HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at
/.well-known/glama.jsonon the same origin as the connector, then choose Check HTTP challenge.DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
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The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
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Discussions
Hi Anton here, I am a former professional Athlete and build this mainly for myself. I would appreciate your thoughts about this project. I personally use it everyday, especially the new "visualization" tool in Claude makes this 10x more powerful.
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Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
Most tools have distinct purposes, but get_activity_data and get_workout_list overlap significantly, and get_daily_summary and get_recovery_status both include HRV/resting HR. Two mutation tools lack descriptions, making them ambiguous.
The 'get_' prefix is consistent for retrieval, but mutation tools use different verbs (delete, plan, update) and inconsistent noun forms (planned_workout vs workout). Older tools like get_workout_list, get_workout_detail, get_workout_samples follow a uniform pattern.
19 tools is slightly high but justifiable for a comprehensive fitness data server covering activities, sleep, nutrition, body, recovery, trends, and workout details. Some tools could potentially be merged, but overall the count is reasonable.
The tool surface covers most common health/fitness data types, but two tools (plan_workout, update_planned_workout) have no descriptions, creating dead ends. Missing direct getters for some metrics (e.g., steps alone) but covered through summaries.