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Fino-wind

Vaultbeat MCP Server

Total energy burned (TDEE)

get_total_energy_burned
Read-onlyIdempotent

Calculate TDEE from actual Apple Watch calorie measurements, returning daily basal/active breakdown and average to set accurate diet targets.

Instructions

TDEE (total daily energy expenditure) = basal + active per day, last N days.

The truthful daily calorie burn from Watch's actual measurements β€” not a formula. Diet targets need to aim BELOW this to lose weight (e.g. eating avg_tdee - 500 = ~0.5 kg/week loss). Returns per-day breakdown {day, basal_kcal, active_kcal, total_kcal, basal_missing, partial, basal_hours_covered, basal_hours_expected, basal_incomplete} + average TDEE. Three kinds of day are excluded from the average and each is listed with its reason in average_excluded_days: today (partial, still accumulating), days with no basal data (basal_missing), and days whose Watch coverage was short (basal_incomplete β€” e.g. 16 of 24 hours). A short day's kcal is low in proportion to the hours it missed, so including it drags the average down and, since the error is one-directional, never cancels out. Quote average_tdee_kcal for diet targets, and if you quote a single day, check basal_incomplete first. Use owner prefix to filter by person.

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. πŸ”΄ 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 β€” 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
NameRequiredDescriptionDefault
daysNo
freshNo
ownerNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.8.1

TDQS

A4.1/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, and non-destructive, but the description adds substantial behavioral detail: excluded day categories, one-directional error from incomplete days, coverage semantics, and how more_available affects reported history. This goes far beyond what the annotations provide.

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

Conciseness2/5

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

The description is front-loaded with the core definition, but it is very long and repeats concepts like coverage, exclusions, and quoting guidance across multiple sentences. The emoji warning and repeated operational instructions add verbosity; it could be tightened substantially without losing key information.

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

Completeness4/5

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

Given the tool's complexity, the description covers the crucial runtime semantics: exclusions from averages, coverage blocks, pagination via more_available, and diet-target interpretation. The missing 'fresh' parameter and the 'limit' naming inconsistency prevent full completeness.

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?

With 0% schema description coverage, the description must explain parameters; it does explain days/last N days and owner prefix. But it never mentions the 'fresh' parameter, and it refers to a 'limit' parameter that does not exist in the input schema, presumably meaning 'days', which can confuse an agent.

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 states a specific verb and resource: it retrieves TDEE as 'basal + active per day, last N days' from Watch measurements. It clearly distinguishes itself from siblings like get_basal_energy by emphasizing the total-energy scope and the per-day breakdown plus average.

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?

Strong usage guidance is present: quote average_tdee_kcal for diet targets, check basal_incomplete before quoting a single day, and interpret coverage.window_satisfied:false as a shorter history. However, it never names sibling alternatives or explicitly states when not to use this tool, so it stops short of a 5.

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