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ApparelHub-AI

apparelhub-mcp

Official

analytics_timeseries

Read-only

Retrieve zero-filled KPI trend series over a date range, bucketed by day, week, or month, with revenue, profit, COGS, orders, units, AOV, and margin metrics.

Instructions

KPI trend series over a date range, bucketed by day, week, or month (zero-filled). Each bucket carries gross revenue, gross profit, COGS, order count, units, AOV, average margin, and margin coverage. Requires an Advanced Analytics plan. Read-only.

[#48246d]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNoEnd date (YYYY-MM-DD). Omit to default to today (UTC).
startNoStart date (YYYY-MM-DD). Omit to default to 30 days before end.
storeNoStore uuid to narrow to one store. Omit for all accessible stores.
currencyNoReporting currency (e.g. "USD"). Currencies are segmented, never summed.
intervalNoBucket granularity (default day).
workspaceNoWorkspace uuid to scope to (agency accounts). Omit for the Default workspace.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.15.2

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already cover read-only and open-world, so the description adds value with the plan gate, the 'zero-filled' bucket behavior, and the enumeration of returned metrics. It does not disclose result-size or rate limits, but the extra behavioral context above the annotations is meaningful.

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?

Three front-loaded sentences that lead with the purpose and bucket granularity before the plan requirement and metrics list. The trailing '[#48246d]' token is stray noise that does not earn its place, keeping this from a 5.

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, enumerating the returned metrics per bucket is genuinely useful, and the plan prerequisite is captured. Date defaults and enum values live in the schema, so the description is essentially complete for a moderate-complexity read tool.

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 coverage is 100%, so the schema already documents all six parameters including defaults and the interval enum. The description only restates the day/week/month bucketing and date-range concept, adding no format or nuance beyond the schema; baseline 3 applies.

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?

States a specific resource and scope: a 'KPI trend series over a date range, bucketed by day/week/month'. This clearly separates it by shape from time-point siblings like analytics_summary or analytics_breakdown, though it never names those siblings explicitly to route the agent.

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

Usage Guidelines3/5

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

Usage is implied (reach for this when you need KPI trends bucketed over time) and one prerequisite is stated ('Requires an Advanced Analytics plan'), but there is no explicit guidance on when to choose this over analytics_summary, analytics_breakdown, or analytics_ops.

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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