WhenPeak

whenpeak_quick_predict

Predict one day's cognitive performance curve from a single night of sleep
the user describes to you.

Returns the 24-hour curve, the primary and secondary peak times, the natural
afternoon dip, the estimated chronotype, a readiness score and a confidence
level, so you can tell the user when to put demanding work and when to
protect the dip.

Choose this tool when the question is about one specific day. Use
whenpeak_multiday_predict for a span of several days. Use
whenpeak_performance_now when the question is about this moment and the
server is configured with the user's own account.

Public and keyless: no API key is required. Read-only, with no side effects.
Nothing is stored, no account is created or modified, and the sleep values
passed in are not retained.

Args:
    wake_time: this morning's wake time, "HH:MM" (e.g. "07:30")
    sleep_time: last night's sleep time, "HH:MM" (e.g. "23:00")
    sleep_quality: "good" | "fair" | "poor"
    exercise_yesterday: whether the user exercised yesterday. Leave unset if
        unknown rather than guessing False.
    exercise_timing: "morning" | "afternoon" | "evening". Leave unset if
        unknown.

Input Schema

TableJSON Schema

Name	Required	Default
`wake_time`	Yes
`sleep_time`	Yes
`sleep_quality`	No	fair
`exercise_timing`	No
`exercise_yesterday`	No

TDQS

A5/5.0

Behavior5/5

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

With no annotations provided, the description carries the full disclosure burden and meets it thoroughly: it declares 'Public and keyless: no API key is required,' 'Read-only, with no side effects,' and explicitly states that nothing is stored, no account is created or modified, and sleep values are not retained. This goes well beyond typical mutation/safety ambiguity.

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 the core purpose and output list, followed by compact sibling routing and a concise safety statement, then a structured Args block. Every sentence adds decision-relevant information or parameter semantics, with no fluff or repetition of the schema title/type.

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, the description explains the return values semantically ('24-hour curve, the primary and secondary peak times... readiness score and confidence level'). It also covers all parameters, safety, and routing, making it complete for an agent to select and invoke the tool correctly.

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 description must compensate, and it does. It documents each parameter with formats ('HH:MM'), enums ('good' | 'fair' | 'poor'), and nuanced null semantics ('Leave unset if unknown rather than guessing False'), covering all five parameters and adding guidance absent from the raw 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: 'Predict one day's cognitive performance curve from a single night of sleep.' It enumerates the returned signals (curve, peak times, dip, chronotype, readiness, confidence) and clearly differentiates itself from siblings by naming whenpeak_multiday_predict and whenpeak_performance_now.

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 states when to use this tool: 'Choose this tool when the question is about one specific day.' It then names the exact alternatives and their conditions: multiday for spans, performance_now for the current moment with a user-configured server. This gives an agent crisp routing logic.

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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