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ApparelHub-AI

apparelhub-mcp

Official

channel_opportunities

Read-only

Identify listings with proven traffic but broken conversion, ranked by missed demand, so you can fix them first and archive genuinely dead items safely.

Instructions

The listings wasting the most demand: proven traffic, broken conversion, ranked by how many people saw them and did not buy. This is the natural starting point for an optimisation pass — fix these before touching anything else, because the demand is already there and only the listing is in the way. Also returns per-state counts and, separately, the listings that are genuinely inert (state "dead") and therefore safe to archive. Nothing else is safe to archive. READ shop BEFORE acting on anything else here. If the shop as a whole is getting almost no views, safe_to_archive will be empty and top_opportunities will be thin — not because the listings are fine, but because nothing has been seen enough to judge. That is a distribution problem and no listing edit will move it. Read-only.

[#7c8c30]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNoEnd date (YYYY-MM-DD), channel-local. Defaults to yesterday.
startNoStart date (YYYY-MM-DD), in the sales channel's own local dates. Defaults to 28 days back.
storeNoNarrow to one store uuid.
providerNoNarrow to one sales channel, by name.
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

A4.6/5.0
Behavior5/5

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

Annotations already cover readOnlyHint, and the description reinforces it with 'Read-only.' Beyond that it discloses return shape (per-state counts, safe_to_archive, top_opportunities), the archive-safety guarantee ('Nothing else is safe to archive'), and a meaningful edge case where empty results signal a distribution problem rather than healthy listings.

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?

Front-loaded with the core definition, then usage, then edge cases — a sensible ordering. Every sentence carries information, but the paragraph is long and contains a stray artifact ('[#7c8c30]') that adds noise.

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?

With no output schema, the description carries the full burden of explaining returns, and it delivers (categorical outputs, per-state counts, archive list). It also covers the interpretation caveat an agent needs to avoid misreading thin results. Nothing essential is missing for correct invocation.

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 all five parameters (start, end, store, provider, workspace) are already fully documented in the schema. The description adds no format or default details beyond what the schema provides, so the baseline 3 applies.

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?

States a specific resource and its defining characteristic up front — listings with proven traffic but broken conversion, ranked by viewers-who-didn't-buy. An agent can immediately distinguish it from analytics_summary or channel_performance siblings. The phrasing is slightly metaphorical but is clarified in the same sentence.

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 positions it as 'the natural starting point for an optimisation pass' and instructs to fix these before anything else. It also sequences a prerequisite ('READ `shop` BEFORE acting') and explains when the output is misleading (low shop views). Rare, high-value when-to-use guidance.

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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