Skip to main content
Glama

fitbit_sync

Sync Fitbit health data into a local cache for fast, offline queries. Use before querying with other Fitbit tools to update data.

Instructions

Sync Fitbit health data to the local cache.

Fetches data from the Fitbit API and stores it in SQLite for fast offline queries. Run this before using other fitbit_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". 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 fitbit_get_heart_rate, fitbit_get_activity, fitbit_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?

With no annotations, the description carries the full burden and excels: it explains incremental sync, first-sync behavior, the role of 'since'/'until' for backfill and gap repair, and the return summary. This goes beyond basic semantics and provides operational context.

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 lengthy but well-structured: a one-line summary, a brief context statement, a clear argument section, and a closing note on return value. Every sentence adds information, and the structure makes it scannable.

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 (four parameters, incremental logic, special backfill scenarios) and the empty schema/annotations, the description covers all necessary ground: purpose, usage, parameter semantics, and return behavior. It is complete enough for an agent to invoke correctly without additional clarification.

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?

The input schema has no descriptions (0% coverage), so the description is entirely responsible for parameter meaning. It thoroughly explains data_types with options and default, days with its behavior, and since/until with precise usage examples. This fully compensates for the schema gap.

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 a specific verb+resource statement: 'Sync Fitbit health data to the local cache.' It clearly distinguishes itself from the fitbit_get_* tools by stating it fetches data from the Fitbit API into SQLite, not for querying.

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?

Explicitly instructs to 'Run this before using other fitbit_get_* tools' and provides a clear exclusion: 'Not for querying data - use fitbit_get_heart_rate, fitbit_get_activity, fitbit_get_sleep, etc. instead.' This leaves no ambiguity about when to use 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/fitbit-mcp'

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