WhenPeak

whenpeak_multiday_predict

Project cognitive performance across the next 7 to 30 days from a single
night of sleep the user describes to you.

Returns one curve, peak, dip and score per day, plus the best and worst
projected days. This repeats one self-reported baseline forward with
decaying confidence, so treat it as the shape of a typical day rather than a
prediction for each individual day. A behavioural forecast that learns
weekday against weekend patterns needs connected sleep history in the
WhenPeak app.

Choose this tool for a span of days. Use whenpeak_quick_predict for one
specific day, and call it once rather than looping it per day.

Public and keyless: no API key is required. Read-only, with no side effects.
Nothing is stored.

Args:
    wake_time: this morning's wake time, "HH:MM"
    sleep_time: last night's sleep time, "HH:MM"
    sleep_quality: "good" | "fair" | "poor"
    days: horizon, 7-30 (default 7)

Input Schema

TableJSON Schema

Name	Required	Default
`days`	No
`wake_time`	Yes
`sleep_time`	Yes
`sleep_quality`	No	fair

TDQS

A5/5.0

Behavior5/5

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

With no annotations present, the description fully carries the burden and is unusually thorough: it discloses read-only status, no side effects, no storage, no API key, decaying confidence, and that output is a typical-day shape rather than per-day certainty. It also states the returned artifacts (curve, peak, dip, score, best/worst days).

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 front-loaded with purpose, then output, then caveats, routing, safety, and args. Every sentence adds information; the Args block is scannable and the caveats are meaningfully placed.

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?

Despite having no output schema and no annotations, the description tells the agent what the tool returns, how to interpret it, when to choose it, how to avoid misuse, and how to fill every parameter. Nothing required for a correct first call is missing.

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?

Schema description coverage is 0%, so the Args block is the only documentation; it compensates fully with semantic meaning and formats: wake/sleep time meanings plus 'HH:MM', sleep_quality enumerations, and days horizon with default. This is materially more informative than the bare schema.

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 and resource: 'Project cognitive performance across the next 7 to 30 days from a single night of sleep.' It also names the sibling it is not (whenpeak_quick_predict) and states it selects spans, so an agent can distinguish it without inspecting schemas.

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?

Gives explicit routing: 'Choose this tool for a span of days. Use whenpeak_quick_predict for one specific day, and call it once rather than looping it per day.' Also flags the need for connected sleep history when weekday/weekend learning is required, a concrete when-not-to-use caveat.

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

All WhenPeak tools Inspect

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TDQS

A4.9/5.0

Disambiguation5/5

Each tool targets a distinct temporal scope: right now, today's best window, a single day, or a multi-day span. The descriptions explicitly cross-reference when to use each, so an agent should not confuse them.

Naming Consistency4/5

All tools share the whenpeak_ prefix and consistent snake_case, making the set recognizable. However, the suffixes mix noun phrases (best_window, performance_now) with verb phrases (multiday_predict, quick_predict), so the pattern is not perfectly uniform.

Tool Count5/5

Four tools is a well-scoped size for this server's purpose: assessing current performance, finding a window today, predicting one day, and projecting multiple days. No tool feels redundant or missing.

Completeness5/5

The tool surface covers the main prediction workflows: current state, today's slot, single-day curve, and multi-day forecast. Since the server is read-only prediction, CRUD operations are not expected, and there are no obvious dead ends.

Resources

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