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create_coupon

Generate discount codes for Thinkific promotions to offer students reduced pricing on courses. Requires a promotion ID from list_promotions.

Instructions

Create a new coupon/discount code. Requires a promotion_id — use list_promotions first to get one.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promotion_idYesPromotion ID this coupon belongs to (use list_promotions to find IDs)
codeYesCoupon code string
noteNoInternal note about this coupon
quantityNoMaximum uses (omit for unlimited)
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions the 'promotion_id' requirement, it doesn't describe what happens when a coupon is created (e.g., whether it's immediately active, what permissions are needed, or what the response looks like). For a mutation tool with zero annotation coverage, this is a significant gap.

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 extremely concise with just two sentences that both earn their place. The first sentence states the purpose, and the second provides crucial usage guidance. There's zero waste or redundancy.

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

Completeness2/5

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

For a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns, what happens on success/failure, or important behavioral aspects like whether duplicate codes are allowed. Given the complexity of creating a coupon, more context would be helpful.

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 documents all four parameters thoroughly. The description adds minimal value by reinforcing the 'promotion_id' requirement but doesn't provide additional semantic context beyond what's in the schema. 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.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Create a new coupon/discount code') and resource ('coupon/discount code'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'bulk_create_coupons' or 'update_coupon' beyond the 'create' verb, which is why it doesn't reach a perfect score.

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 provides clear context for when to use this tool ('Requires a promotion_id — use list_promotions first to get one'), which helps the agent understand prerequisites. However, it doesn't explicitly state when NOT to use it or mention alternatives like 'bulk_create_coupons' for multiple coupons, so it falls short of a perfect score.

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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