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Pridly — AI copilot for PrestaShop

Customer segments

customer_segments
Read-only

Groups customers into VIP (top 10% by spend), repeat, one-time, new (first order in the last 30 days) and lapsed (no order for 90+ days). Names are shortened and emails are never shown. Pass segment to list who is in one group.

Input Schema

NameRequiredDescriptionDefault
limitNo
storeNoStore address or id. Leave empty if you have only one store.
segmentNo
lookback_daysNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare this a read-only, non-destructive, open-world tool, so the description is freed from covering safety. It adds genuinely new behavioral context: names are shortened and emails are never shown, which is important privacy information an agent would not otherwise know. It stops short of describing return shape or what happens when `segment` is omitted.

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?

Two sentences, front-loaded with the segment definitions, then the privacy note, then the `segment` usage hint. Every clause carries information and nothing is repeated from structured fields except where it clarifies the enum.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, so the description must convey what a call returns. It implies a customer list when `segment` is passed, but never says what the default (no `segment`) response looks like — presumably per-segment counts — and never explains how `lookback_days` or `limit` affect results. For a read-only analytics tool this is adequate but not complete.

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 only 25% (only `store` is documented). The description compensates partially by defining the meaning of each `segment` enum value (VIP top 10% by spend, lapsed 90+ days, etc.), which is real value beyond the bare enum list, and repeats the usage hint for `segment`. However, `lookback_days` (30–730, default 365) and `limit` (1–50, default 10) are completely unexplained in both schema and description, so half the parameters remain opaque.

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 clearly states the resource and the operation: it groups customers into named segments and defines the criteria for each (VIP top 10% by spend, new = first order in 30 days, lapsed = 90+ days without an order). That is strongly specific, but it never distinguishes this from the close sibling customer_insights, so an agent cannot tell the two apart from the text alone.

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?

It gives one concrete usage instruction ('Pass `segment` to list who is in one group'), which implies the default call returns aggregate segments. But it offers no when-to-use vs when-not guidance, no mention of customer_insights as an alternative, and no note on when to omit `segment`.

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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