Skip to main content
Glama
partymola

google-health-mcp

health_sync

Sync health data into a local SQLite cache for fast offline queries. Fetch incremental updates from Google Health API, specifying data types and date ranges to populate or repair cached records.

Instructions

Sync health data to the local cache.

Fetches data from the Google Health API and stores it in SQLite for fast offline queries. Run this before using other health_get_* tools.

Syncs incrementally: only fetches data newer than the most recent entry in each table. First sync fetches the specified number of days.

Args: data_types: What to sync. Options: "all", "heart_rate", "activity", "exercises", "sleep", "weight", "spo2", "hrv", "azm", "breathing_rate", "skin_temperature", "core_temperature", "cardio_fitness", "food_log", "ecg", "irn". Comma-separated for multiple, e.g. "sleep,hrv". Default: "all". days: Days of history for first sync (default: 30). Ignored on subsequent syncs (uses last synced date). since: Optional "YYYY-MM-DD" backfill date. When set, fetches from this date regardless of what is already cached - use to pull history older than the current cache. Overrides incremental resume and days. until: Optional "YYYY-MM-DD" inclusive end date; requires since. Together they re-fetch and upsert exactly the since..until window - use to repair a gap in the middle of the cache without re-pulling everything from the gap to today.

Returns summary of records synced per data type. Not for querying data - use health_get_heart_rate, health_get_activity, health_get_sleep, etc. instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo
sinceNo
untilNo
data_typesNoall

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It thoroughly explains that the tool fetches from the Google Health API and stores in SQLite, syncs incrementally based on the latest cache entry, and that since/until will re-fetch and upsert a specific window. These are meaningful behavioral traits beyond a simple 'sync' label, and the description is transparent about the side effects (writes to cache) and edge cases (backfill, gap repair, ignoring 'days' on subsequent syncs).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded. It leads with the core purpose, then usage context, then parameter details, and closes with return value and alternative tools. Every sentence provides necessary information, and the parameter docs are formatted clearly. No filler or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (4 parameters with nuanced behavior, incremental sync, backfill, window repair) and the presence of an output schema, the description is complete. It covers prerequisites, return summary, parameter semantics, and explicitly states what the tool is not for. It leaves no significant gaps for an agent to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It does so comprehensively: data_types lists all acceptable values, comma-separated usage, and default; days explains its role for first sync and that it is ignored later; since and until describe their backfill and repair semantics, including that until requires since and that together they re-fetch exactly the specified window. This adds rich meaning beyond the bare schema definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Sync health data to the local cache,' which clearly identifies the verb (sync) and resource (health data to local cache). It further distinguishes itself from sibling tools by stating 'Run this before using other health_get_* tools' and 'Not for querying data - use health_get_heart_rate ... instead.' This makes the purpose unambiguous and differentiates it from the many read-only getter tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit usage guidance: it should be run before using other health_get_* tools to populate the cache, and it is not for querying data. It also explains the incremental sync behavior and when to use since/until for backfill or gap repair, providing clear context for when to invoke this tool versus alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/partymola/google-health-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server