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davidmosiah

Wellness Cycle Coach

Cycle estimate phase

cycle_estimate_phase
Read-onlyIdempotent

Estimate current menstrual cycle phase, day, next period date, and confidence from recent period start dates. Supports irregular cycles with clinician-defer warnings.

Instructions

Given a list of recent period start dates (from any source), returns the current phase, cycle day, estimated cycle length, next-period date, and confidence. v0.3.2 adds a late_luteal sub-phase (triggered when the cycle is past its expected end + grace day) plus days_past_due and delay_flag (raised when ≥2 days late vs prediction from 3+ historical cycles). v0.3.3 adds cycle_irregular (PCOS / irregular-cycle mode): when true, accepts cycles 21-90 days, caps confidence at 'low', returns luteal_extended + irregular_window: true past 35 days since last period, and adds a clinician-defer warning.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
todayNoOptional reference date; defaults to system today.
historyYesArray of {start_date: 'YYYY-MM-DD', length_days?: number}. Sorted automatically.
privacy_modeNoOptional privacy mode: summary | structured | raw. summary omits free-text notes when present; structured/raw return full payload.
cycle_irregularNov0.3.3 — PCOS / irregular-cycle mode. When true: accepts cycles 21-90 days, caps confidence at 'low', returns `luteal_extended` when days_since_last > 35, and adds a clinician-defer warning string. Default false.
Install Server

TDQS

A4.1/5.0
Behavior5/5

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

The description goes well beyond the readOnly/idempotent annotations by explaining nuanced behaviors: late_luteal sub-phase triggers, days_past_due, delay_flag thresholds, cycle_irregular modes, confidence capping, and clinician-defer warnings. This gives an agent strong expectations of how the tool behaves in different situations.

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 core purpose is front-loaded in the first sentence, and the version-specific details are organized with code-formatted field names. The description is somewhat long due to changelog-style additions, but each part conveys meaningful behavioral context rather than filler.

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?

With no output schema, the description enumerates the primary return fields and important edge cases (late luteal, irregular mode, delay flag). It is sufficient for invoking the tool correctly, though it leaves some details like the exact confidence scale or output formatting implicit.

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 description coverage is 100%, so the schema already documents all parameters. The description adds some context for history ('from any source') and repeats cycle_irregular behavior, but it does not substantially enrich the semantics of today or privacy_mode beyond what the schema states.

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

Purpose4/5

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

The description clearly identifies the tool's function: given a list of recent period start dates, it returns current phase, cycle day, estimated cycle length, next-period date, and confidence. This is a specific verb-resource combination, though it does not explicitly differentiate itself from sibling tools like cycle_predict_next_period.

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?

The description provides clear context by specifying the required input ('Given a list of recent period start dates') and the outputs, so an agent can infer when to use it. It does not discuss exclusions or explicitly name alternatives, but the input condition is reasonably clear.

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

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