Vaultbeat MCP Server

Overview Schema Source Related Servers Discussions Admin

Symptoms

get_symptoms

Read-onlyIdempotent

Decrypt and review recent symptoms grouped by owner, including Apple Health days and self-reported episodes, to track health history.

Instructions

Decrypt recent symptoms locally, grouped by data owner — both sources.

SENSITIVE. Each entry in owners carries owner_user_id and two lists: · days — symptoms imported from Apple Health (cramps, headache, fatigue, coughing…), one row per day with each sample's severity (mild/moderate/severe/present/…). These reach this server only when the user opted in on iOS — their own AI toggle, or the partner-AI toggle for a partner's. symptom_counts counts logged DAYS per type. · reported — episodes a person reported themselves, through log_symptom or the Vaultbeat app: entry_id, symptom_type, display_name (their own words), severity, local_date, onset_at / end_at (end_recorded: false means no end was logged — ask, do not assume it is still going), duration_minutes, body_location, triggers, note. reported_counts counts EPISODES per type. A type that exists in Apple Health is spelled the same in both lists (healthkit_type: true), so "headache" from either source is one symptom. To correct or remove a reported entry use update_symptom / delete_symptom with its entry_id; imported days are edited in Apple Health. Stays on-device, never re-exported.

Returns YOUR symptoms by default; partner=true returns your partner's Apple Health days only (if they share symptoms with you from their own app). A partner's reported episodes never reach you: the app and log_symptom seal them for their owner's own AI alone, so an empty reported under partner=true means "not shareable", not "none logged".

Carries a coverage block: quote coverage.days_covered (distinct days, not the row count) and coverage.span_days beside any average or trend. coverage.window_satisfied: false alone does NOT mean a short history: it is also false when older days exist beyond your window. 🔴 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. limit cuts each list on its own: that many imported days AND that many reported entries, so a day with three reports can come back with one.

Input Schema

Name	Required	Description	Default
`fresh`	No
`limit`	No
`partner`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv0.9.1Oct 3, 2026
- addedInput schema / additionalProperties
```
Added value: +false
```
- addedInput schema / properties / partner
```
Added value: +{
+  "default": false,
+  "title": "Partner",
+  "type": "boolean"
+}
```
First observedv0.8.1Sep 23, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Annotations already establish a safe read (readOnly, idempotent, non-destructive), and the description adds substantial context beyond them: sensitive on-device data never re-exported, partner sharing semantics ('empty reported means not shareable, not none logged'), coverage-block caveats, and the warning that each list is cut independently by `limit`. This is exactly the behavioral depth the dimension rewards.

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?

Front-loads the core purpose and uses bulleted structure to separate the two data sources, with the load-bearing limit caveat at the end. It is dense and long, but nearly every sentence carries actionable information; only the repetitive coverage-block explanation could be tightened.

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?

Output schema exists, yet the description still supplies the interpretive schema the agent needs: field-level meaning, count semantics (days vs episodes), the coverage quotation rule, and the limit-trap warning. For a sensitive, multi-source, share-gated tool, nothing essential to correct invocation is missing.

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

Parameters3/5

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

Schema coverage is 0%, so the description must carry all parameter meaning. It richly explains `partner=true` and the per-list `limit` behavior (including the 'one day can return one report' warning), but `fresh` is never mentioned, leaving one of three parameters undocumented in both schema and description.

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 symptoms locally, grouped by data owner') and immediately distinguishes the two sources (Apple Health 'days' vs self- 'reported' episodes). An agent can tell it apart from log_symptom/update_symptom/delete_symptom, which are explicitly named as the write/delete path.

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

Usage Guidelines4/5

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

Clear context: it routes corrections/removals to update_symptom/delete_symptom, explains that imported days are edited in Apple Health, and defines the partner=true scope. It lacks an explicit 'use this when...' trigger and does not explain the `fresh` flag's role, so it stops short of full when/when-not guidance.

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