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Churn Risk

customers_churn
Read-onlyIdempotent

Identify customers at risk of churning using RFM recency and frequency signals. Returns segmented arrays with win-back recommendations for targeted re-engagement.

Instructions

Identify customers at risk of churning based on RFM recency + frequency signals. Returns an object with at_risk, hibernating, and lost arrays — each contains customer id, name, email, last_order_date, days_since_last_order, total_spent, total_orders, and a win_back_recommendation string. Use this for targeted re-engagement campaigns.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
store_idYesUUID of a connected store (returned by store_connect with action="connect" or visible in store_connect with action="list" / the store_overview resource)
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds value by detailing the return object structure (at_risk, hibernating, lost arrays with specific fields and win_back_recommendation), which is beyond what annotations provide.

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?

The description is two sentences, front-loaded with the core function, and efficiently conveys purpose and return format without any fluff. Every sentence earns its place.

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?

Given the low complexity (1 parameter, no output schema), the description covers purpose, usage, and return structure comprehensively. Annotations provide safety traits. No gaps remain.

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% with a single well-described parameter (store_id). The description adds no extra meaning beyond the schema, so baseline 3 is appropriate.

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?

The description uses specific verb+resource ('Identify customers at risk of churning') and clearly distinguishes from sibling tools like customers_segment and order_anomalies by focusing on churn risk based on RFM signals.

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

Usage Guidelines4/5

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

The description explicitly states 'Use this for targeted re-engagement campaigns,' providing clear context. It does not mention when not to use or list alternatives, but the use case is well-defined.

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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