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rushikeshmore

Shopify Partner Agent

get_merchant_health

Evaluate merchant health with a composite A-F grade, combining tenure, revenue, stability, and engagement into a single score.

Instructions

Score merchants with a composite health grade (A-F).

Args: app_id: App GID or numeric ID (required).

Grades based on 4 dimensions (0-25 each, total 0-100):

  • Tenure: how long installed

  • Revenue: total and recent charges

  • Stability: no deactivations/reinstalls

  • Engagement: recent activity

A (85+), B (70-84), C (55-69), D (40-54), F (<40).

Returns: JSON string with grade distribution summary and per-merchant health scores with dimensional breakdown.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
app_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations provided, the description carries the full burden. It fully discloses the composite scoring methodology, dimensions, grade thresholds, and return format, which is substantial behavioral context beyond a simple one-liner. It doesn't mention potential side effects or data freshness, but for a read-only analytics tool, this is adequate.

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 well-structured with a clear opening statement, an Args section, a concise breakdown of grading dimensions, and a Returns section. Every sentence adds value, and the length is appropriate for the tool's complexity.

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?

The description explains the purpose, parameters, grading logic, thresholds, and output structure. Despite the tool's complexity, the description provides enough context for an agent to correctly invoke and interpret results. The presence of an output schema reduces the need to explain return types further, but the description already covers the key details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema only shows app_id as a string without description. The description compensates fully by explaining app_id as 'App GID or numeric ID (required)', adding critical format and requirement context beyond the schema.

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 clearly states 'Score merchants with a composite health grade (A-F)' using a specific verb (score) and resource (merchants with health grade). It distinguishes from siblings by specifying the composite health grade focus, which differs from churn, retention, or revenue tools.

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 clearly indicates it is for scoring merchant health, and the required app_id is specified. While it doesn't explicitly state when not to use it or name alternatives, the context is clear enough that an agent would understand when to invoke it.

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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