fetch-garmin-activity

Fetch Garmin Activity

Connects to the nrvim/garmin-givemydata MCP server (the Taxuspt/garmin_mcp server is no longer supported — Garmin's March 2026 Cloudflare deployment broke garth-based servers). Triggers a live sync to refresh the local SQLite DB, queries recent run activity IDs, fetches per-activity detail, normalizes each to the internal workout schema via scripts/garmin_normalize.py, deduplicates against existing workouts.json entries, and runs the same analyze-workout + adapt-plan pipeline as manual logging.

When to use

• DataFetcher agent is invoked via /run-sync.
• users.json.integrations.garmin.connected == true (set by a successful /run-init --connect garmin probe).

Architecture

garmin-givemydata exposes a SQLite-backed MCP server. Read tools (garmin_activities, garmin_activity_detail, garmin_query, garmin_user_profile) hit the local DB only and never touch Garmin live. The single tool that does live Garmin access is garmin_sync, which uses SeleniumBase UC mode (undetected Chrome) to bypass Cloudflare. This means all auth/Cloudflare failures surface in the garmin_sync step — the read tools afterwards always succeed (or surface SQLite errors, not network errors).

Normalization

Field mapping is implemented in scripts/garmin_normalize.py — call normalize_activity(detail) on each garmin_activity_detail response. The detail dict has top-level keys activity, splits, hr_zones, weather, exercise_sets, running_dynamics — pass the whole dict, not just activity. The function returns None for non-run activities, activities shorter than 5 minutes, activities with zero distance, or activities missing activity_id; skip those silently.

Outputs

Normalized workout records appended to storage/workouts.json (via upsert_workout from garmin_normalize.py), analysis and adapt-plan signals applied, users.json.integrations.garmin.last_sync_at and last_sync_status updated.

See body.md for the full step-by-step instructions, error handling, and deduplication semantics.