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Glama
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Server Details

63 tools for Apple Health, Fitbit, Oura & Health Connect data in Claude, ChatGPT, Grok & Mistral.

Status
Unhealthy
Last Tested
Transport
Streamable HTTP
URL
Repository
turnnoblindeye/wellness-project-mcp
GitHub Stars
0
Server Listing
Wellness Project MCP

Glama MCP Gateway

Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.

MCP client
Glama
MCP server

Full call logging

Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.

Tool access control

Enable or disable individual tools per connector, so you decide what your agents can and cannot do.

Managed credentials

Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.

Usage analytics

See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.

100% free. Your data is private.
Tool DescriptionsA

Average 4.6/5 across 59 of 59 tools scored. Lowest: 3.9/5.

Server CoherenceA
Disambiguation4/5

Each tool targets a distinct domain and action, and descriptions are detailed enough to separate similar tools (e.g., show_week_workouts vs get_workout). However, the sheer number of list_*/log_*/delete_* tools plus combined manage_* tools could occasionally cause misselection, especially between manage_recovery_strategy and log_recovery_session.

Naming Consistency5/5

The set follows a consistent verb_noun snake_case pattern throughout, with predictable families: list_*, log_*, update_*, delete_*, get_*, show_*, and manage_*. The show_week_* and show_* prefixes clearly indicate visualization tools.

Tool Count2/5

59 tools is far beyond the typical well-scoped range (3-15). While the fitness domain is broad, the surface could be consolidated (e.g., combined CRUD managers like manage_supplement; many delete_* could fold into update or manage operations). The large selection space increases agent confusion and latency.

Completeness4/5

The surface covers CRUD/lifecycle for nearly every domain (workouts, meals, sleep, cycle, injuries, labs, supplements, recovery, wellbeing, wearables, runs, body metrics), with upserts covering update for several, and rich analytics via show_* tools. Minor gap: no update_run, so correcting a run requires delete and re-log; some domains lack explicit delete (wearables, sleep, body metrics) but rely on upserts instead.

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