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wasintoh

line-oa-mcp-ultimate

by wasintoh

Get LINE coupon redemption stats

line_get_coupon_stats
Read-onlyIdempotent

Combine coupon details and optional click-audience data to retrieve estimated redemption numbers and raw stats for your LINE coupon.

Instructions

Read available stats for a LINE coupon. We combine multiple signals because LINE's Coupon API does not (yet) expose a dedicated redemption-events endpoint:

  1. Raw coupon detail (LINE GET /v2/bot/coupon/{id}) — some accounts include usage fields inline.

  2. Click-audience size (if you pass click_audience_id) — proxies "users who clicked through".

Args:

  • coupon_id: ID of the coupon (from line_manage_coupon mode='list' or mode='create').

  • click_audience_id: optional numeric audience id to enrich the report.

  • oa: optional OA id.

  • response_format: 'markdown' (default) | 'json'.

Returns (structured): { coupon_id, raw: {...whatever LINE returned about the coupon...}, click_audience?: { id, name, audience_count, status }, estimated_redemptions?: number, notes: string[] // explains data sources clearly }

Notes:

  • For an accurate redemption count, build your coupon Flex with a postback action and listen for postback events via a webhook server (V2 webhook-server companion).

  • Click audience size approximates "clickthroughs", not actual redemptions — surface this caveat to the user.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
coupon_idYes
click_audience_idNoOptional — if you created a click audience from the coupon broadcast, pass its id to enrich the stats with click-through count.
oaNo
response_formatNomarkdown
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context about data source combination, return structure, and caveats about clickthroughs vs redemptions, going well beyond the annotations.

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 clear sections, bullet points, and a dedicated Args list. Every sentence provides essential information without redundancy, making it efficient for an agent to parse.

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?

Despite no output schema, the description includes a structured return format with fields. It covers data sources, optional parameters, caveats, and references to sibling tools, making it comprehensive for a stats tool with complex behavior.

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?

Schema description coverage is only 25%, but the description explains all four parameters in detail. It specifies the source for coupon_id (from list/create mode), optional enrichment via click_audience_id, and the purpose of response_format. This adds significant meaning 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 the tool reads coupon stats, combining multiple data sources. It distinguishes itself from sibling tools by focusing specifically on coupon-related statistics, which is unique among siblings like line_get_message_stats.

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

Usage Guidelines5/5

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

The description provides explicit when-to-use and when-not-to-use guidance. It explains that for accurate redemption counts, agents should use postback actions and webhooks instead, and it cautions that click audience size is an approximation.

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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