Vaultbeat MCP Server

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Heart rate variability

get_hrv

Read-onlyIdempotent

Decrypt and return HRV (SDNN) records from Apple Health, with hourly buckets for trend analysis or raw samples for precise spikes. Includes coverage metadata and owner filter.

Instructions

Decrypt recent HRV (SDNN in ms) — returns records plus average over the window.

granularity selects between two backing kinds:

"hourly" (default) — routes to hrv_hourly kind: one bucket per UTC hour (arithmetic mean of every raw SDNN sample in the hour). 30-day rolling window, ≤720 records, includes sample_count per bucket. Records also carry a sdnn_ms alias equal to the hourly mean, so callers migrating from the pre-build-77 raw default keep working without a field rename. Right for trend / aggregate queries — SAVES CONTEXT vs raw.
"raw" — routes to hrv kind: one record per SDNN sample (Apple Watch emits every 5-15min). 3-day rolling window; older raw history lives in prior-recipient envelopes plus the VaultbeatHistoryBackfillCoordinator-driven historical push (advances 30d/24h on device wake-ups, up to 5 years). Use for spike-precision questions (e.g. "HRV during the 3 minutes I opened a stressful message"). Note: single-day count is often 30-100+ records.

⚠️ average_sdnn_ms from the two granularities is NOT directly comparable — they observe different windows (3d vs 30d) and, on the raw side, also include legacy per-sample blobs from before build 77. Use hourly for "what has my HRV been lately?" trend answers; use raw only when you need per-sample precision inside the last ~3 days. The equivalence claim in previous doc versions was retracted 2026-07-22 after an adversarial review pointed out the window mismatch.

Use owner prefix to filter by person — take it from vaultbeat_status (owner_user_id_prefix is the paired user; server 0.7.1+), or vaultbeat_doctor for every owner present in the data.

Carries a coverage block: quote coverage.days_covered (distinct days, not the row count) and coverage.span_days beside any average or trend, and read coverage.window_satisfied: false as a shorter history than asked, not as a missing kind. 🔴 Before saying how far back someone's data goes, read coverage.more_available: true means this server can decrypt days OLDER than first_day that your limit left behind — re-read with a larger limit, or quote coverage.oldest_available as the real start of their history. Never report a limit-shaped window as the extent of their data.

Input Schema

TableJSON Schema

Name	Required	Default
`fresh`	No
`limit`	No
`owner`	No
`granularity`	No	hourly

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.8.1Sep 23, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already mark it read-only, idempotent, and non-destructive; the description adds substantial behavioral detail: rolling windows, data source (Apple Watch sampling rate), backward-compatibility alias, coverage block semantics, and the `more_available` flag with explicit instructions not to misreport the limit-shaped window as full history. It even retracts a prior equivalence claim, showing careful disclosure.

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

Conciseness4/5

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

The description is long but exceptionally well-structured: purpose front-loaded, granularity explained in a bulleted block, a warning callout, and a dedicated coverage section. Every sentence adds value for a complex tool; it is as concise as the complexity allows, though it could trim minor redundancy in the coverage section.

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?

The description covers all critical operational aspects: window sizes, record counts, sampling patterns, alias behavior, coverage fields, owner sourcing, and the warning about comparability. Combined with the output schema (present) and annotations, an agent has everything needed to call the tool correctly and interpret results.

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

Parameters4/5

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

With schema description coverage at 0%, the description must compensate. It thoroughly explains `granularity` (two modes with exact routing), `owner` (source and format), and `limit` (impact on data extent via `more_available`). However, the `fresh` parameter is not addressed, leaving a small gap; the detailed treatment of the other three parameters still earns a high score.

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?

States a specific verb and resource ('Decrypt recent HRV (SDNN in ms)') and immediately differentiates the two granularity modes. The tool's role among siblings (e.g., get_resting_hr, get_metric_trend) is clear because HRV is a distinct metric.

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 when to use hourly vs raw ('hourly for trend/aggregate answers... raw only when you need per-sample precision'), warns that averages are NOT comparable across granularities, and explains how to source the owner prefix from other tools. This goes beyond vague context into actionable routing.

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