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Peak hours

get_peak_hours
Read-onlyIdempotent

The website's data as one week, hour by hour, in its time zone, for visits, goals, sales or revenue. All time unless range says less.

Input Schema

NameRequiredDescriptionDefault
modelNoRevenue attribution model (defaults to the website's)
rangeNoFor peak-hours: 7d, 30d, 90d, 12m (default all time). For uptime, the response-time chart's: 1h, 24h, 7d, 30d.
metricNoWhat to count (default visits). Sales and revenue are new payments, without renewals or refunds.
filtersNoFilters, each [dimension, operator, value], e.g. [["country","is","US"]]
websiteYesWebsite id or domain, e.g. example.com (see list_websites)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the description's added value is real: it discloses that results are aggregated across all history into a single week-long hourly bucket and rendered in the website's own time zone, which matters for interpretation. It stops short of saying how ties/peaks are determined or how large the response is.

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?

Two short sentences, front-loaded with what the caller gets, followed by the default-range caveat. Phrasing is slightly compressed ('The website's data as one week') but nothing is wasted or buried.

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?

For a read-only, five-parameter analytics query with no output schema, the description conveys the essential return shape (7x24 hourly buckets, site time zone) and the default time span. It leaves the precise response structure and the meaning of 'peak' implicit, but nothing critical to invoking it correctly is missing.

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 model, range, metric, filters and website. The description only restates the metric set and the default range behavior, adding no syntax, filter semantics, or interaction detail beyond what the schema provides.

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 states a clear resource and shape: the website's data collapsed into one week, hour by hour, in the site's own time zone, for one of four metrics. It does not, however, distinguish itself from plausible siblings like get_heatmap or get_timeseries, which an agent could easily confuse with a 7x24 hourly grid.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use guidance and no mention of alternatives; the only guidance is an implicit default ('All time unless `range` says less'). An agent must guess whether this or get_heatmap/get_timeseries is appropriate for hourly-pattern questions.

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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