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refgrow
by refgrow

list_referrals

View and filter referral program signups tracked through affiliate links. Shows which affiliate referred each user and their conversion status.

Instructions

List referred users (signups tracked through affiliate links). Filter by affiliate or conversion status. Shows which affiliate referred each user.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
affiliate_idNoFilter by affiliate ID
statusNoFilter by conversion status
limitNoNumber of results (default 20)
offsetNoPagination offset
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It describes what the tool does (listing with filtering) but doesn't mention important behavioral aspects like whether this is a read-only operation, pagination behavior beyond the offset parameter, rate limits, authentication requirements, or what the output format looks like. The description is functional but lacks operational context.

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?

The description is efficiently structured in two sentences that convey the core purpose and key capabilities. The first sentence establishes the main function, and the second adds important filtering context. There's no wasted verbiage, though it could be slightly more front-loaded with the most critical information.

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?

For a list tool with 4 parameters and no output schema, the description provides adequate functional context but lacks operational completeness. It explains what the tool does and filtering options, but doesn't address output format, pagination behavior, or error conditions. With no annotations and no output schema, users must infer these aspects from the parameter names alone.

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 the schema already fully documents all 4 parameters. The description mentions filtering by 'affiliate or conversion status' which aligns with the schema's affiliate_id and status parameters, but adds no additional semantic context beyond what's in the schema descriptions. This meets the baseline for high schema coverage.

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 the verb ('List') and resource ('referred users'), specifies they are 'signups tracked through affiliate links', and distinguishes from siblings by focusing on referral relationships rather than affiliates, conversions, or coupons directly. It explicitly mentions showing 'which affiliate referred each user', which differentiates it from tools like list_affiliates or list_conversions.

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?

The description implies usage context by mentioning filtering capabilities ('Filter by affiliate or conversion status'), but doesn't explicitly state when to use this tool versus alternatives like get_referral_details or list_conversions. It provides some guidance through the filtering mention but lacks explicit comparisons or exclusions.

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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