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Related Servers

Alternatives to Garmin Local Archive

Related Servers

  • A
    license
    B
    quality
    A
    maintenance
    Downloads all your Garmin health and fitness data into a local SQLite database and exposes 45 MCP tools for AI analysis, enabling assistants to query sleep, training load, HRV, and more.
    48
    183 PyPI
    160
    AGPL 3.0
  • A
    license
    Not graded
    quality
    C
    maintenance
    A local MCP server that exposes your Garmin Connect data—sleep, HRV, training readiness, workouts, and more—to any MCP-compatible AI assistant. Runs entirely on your machine and keeps your Garmin credentials private.
    MIT
  • A
    license
    B
    quality
    A
    maintenance
    Local-first MCP server that connects AI agents to your Garmin sleep, HRV, Body Battery, stress, training readiness and activities, keeping tokens on your machine.
    2
    42
    125 npm
    13
    MIT
  • A
    license
    Not graded
    quality
    A
    maintenance
    MCP server that mirrors your Garmin data into a personal database and exposes tools for health summaries, training load, muscle readiness, and race analysis, with optional chat-driven insights via stdio or HTTP.
    1
    MIT
  • A
    license
    Not graded
    quality
    B
    maintenance
    This MCP server exposes Garmin Connect health data—sleep, heart rate, HRV, stress, VO2max, and activities—to Claude through local tools, enabling natural language queries, data syncing, and statistical analysis like correlations and night-out detection.
    MIT
  • F
    license
    A
    quality
    C
    maintenance
    A local MCP server that exposes Garmin Connect activity, health, and training data as tools for Claude Desktop. It provides read-only access to metrics like activities, sleep, heart rate, and training readiness.
    25
    -

TDQS

A4.1/5.0

Scored across 11 tools

Disambiguation5/5

Each tool has a clearly distinct role: query_health vs query_context split cleanly by body vs external data, baselines vs anomalies vs gaps vs correlate each target a different analytical question, and query_raw is explicitly scoped against query_health with guidance on when to prefer each. The descriptions actively cross-reference and steer the agent away from the most likely confusions.

Naming Consistency4/5

All names are lowercase snake_case, but the pattern mixes verb_noun forms (query_health, refresh_cache, get_archive_metadata, list_available_fields) with bare-noun analytical tools (baselines, anomalies, gaps, correlate). Readable and predictable enough, but not a single consistent convention.

Tool Count5/5

Eleven tools is well within the ideal range and each one earns its place: discovery, health/context querying, raw access, three analytics tools, gap listing, cache refresh, and archive metadata. No redundant or filler tools.

Completeness4/5

For a read-only local archive the query/analytics surface is thorough (field discovery, daily and series queries, raw access, baselines, anomalies, gaps, correlation, metadata, cache refresh). The main gap is that FIT support is entirely non-functional — query_fit_activities is a stub and gaps 'fit' always degrades — which leaves a whole promised domain uncovered.

Maintenance

ActivityActive
ResponsivenessResponsive