Tells your agent when its human peaks, when to protect the dip, and how ready they are today, so it can schedule work around real capacity instead of treating every hour as equal. Returns the peak window, dip, secondary peak, readiness score and chronotype from sleep, plus today's best slot for a given kind of work and whether right now is a peak or a dip.
Find today's best time window for one kind of work, using the stored sleep
history of the account this server is configured with.
Returns a start and end time for the window and the projected capacity
across it, tuned to the kind of work: analytical, creative, learning or
administrative.
Choose this tool when the user wants a slot for a task later today. Use
whenpeak_performance_now for the current moment instead, and
whenpeak_quick_predict when working from sleep the user describes rather
than stored history.
Requires WHENPEAK_API_KEY on the server and reads that one account's
history, so it is only meaningful where the server runs with the user's own
key. Without a key it returns a not_configured error rather than failing.
Read-only and stores nothing, but each call counts against that account's
monthly quota.
Args:
task_type: "analytical" | "creative" | "learning" | "administrative"
duration_minutes: window length in minutes (default 90)
| Name | Required | Description | Default |
|---|---|---|---|
| task_type | No | analytical | |
| duration_minutes | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure, and it excels: it states that the tool is read-only, stores nothing, requires WHENPEAK_API_KEY, reads only that account's history, returns a not_configured error without a key, and each call counts against the monthly quota. It also clarifies the limitation that it's only meaningful where the server uses the user's own key.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-organized and front-loaded with the core purpose, followed by output, usage guidance, and then caveats. Each sentence provides distinct value—no filler. The length is justified by the number of behavioral notes and alternatives it must convey, and it remains readable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there is no output schema, the description adequately explains the return (start/end time and projected capacity). It covers purpose, when to use, requirements, error behavior, quota impact, and parameter semantics. Nothing an agent needs to invoke it correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 lists the allowed values for task_type ('analytical' | 'creative' | 'learning' | 'administrative') and specifies duration_minutes as 'window length in minutes (default 90)'. This adds meaningful semantics beyond the schema's type/default declarations.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Find'), a precise resource ('today's best time window for one kind of work'), states the data source (stored sleep history), and lists the output (start/end time and projected capacity). It also names the four work types explicitly, making the tool's function unmistakable and differentiating it from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives explicit 'when to use' guidance ('when the user wants a slot for a task later today') and names two alternatives with clear conditions ('whenpeak_performance_now for the current moment', 'whenpeak_quick_predict when working from sleep the user describes'). This fully routes the agent to the correct tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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)
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | ||
| wake_time | Yes | ||
| sleep_time | Yes | ||
| sleep_quality | No | fair |
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.
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.
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.
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.
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.
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.
Report the current moment's performance state for the account this server is
configured with: the score right now and whether now is a peak, a dip, or a
neutral window.
Returns the current score, the window type, a plain-language recommendation,
and today's peak and dip times. Meant as a cheap check before an agent
recommends, schedules or starts demanding work.
Choose this tool for "right now". Use whenpeak_best_window to find a slot
later today, and whenpeak_quick_predict when working from sleep the user
describes rather than stored history.
Requires WHENPEAK_API_KEY on the server and reads that one account's
history, so it is only meaningful where the server runs with the user's own
key. Without a key it returns a not_configured error rather than failing.
Read-only and stores nothing, but each call counts against that account's
monthly quota.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and meets it. It discloses the API key prerequisite, that it reads one account's history, that it returns a not_configured error without a key, that it is read-only and stores nothing, and that it consumes monthly quota. This is exemplary transparency beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, then output, then usage and caveats. Although it is longer than many tool descriptions, every sentence adds essential information: outputs, intended use, sibling routing, key requirement, error behavior, and quota impact.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool with no output schema, the description is complete. It explains what the tool reports, what it returns, when to use it, prerequisites, failure behavior, and cost. An agent has enough context to invoke it correctly and interpret the result.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, so the baseline is 4 and there is no parameter detail the description needs to supply. The description correctly focuses on behavior and output instead of inventing parameter-level semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description leads with a specific verb and resource: "Report the current moment's performance state" and names the exact outputs (score, window type, recommendation, today's peak/dip times). It distinguishes itself from siblings by explicitly stating "right now" versus later-today prediction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives explicit when-to-use guidance: "Meant as a cheap check before an agent recommends, schedules or starts demanding work. Choose this tool for 'right now'." It also names alternatives: whenpeak_best_window for a later slot and whenpeak_quick_predict for user-described sleep, so an agent can route correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| wake_time | Yes | ||
| sleep_time | Yes | ||
| sleep_quality | No | fair | |
| exercise_timing | No | ||
| exercise_yesterday | No |
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.
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.
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.
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.
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.
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
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Add one secure layer between your agents and this server.
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.
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.
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.
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.