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wasintoh

line-oa-mcp-ultimate

by wasintoh

Get LINE message engagement stats

line_get_message_stats
Read-onlyIdempotent

Retrieve broadcast message engagement stats: impressions, opens, clicks, CTR, and per-URL click breakdown using a request_id from a prior send.

Instructions

Fetch per-broadcast engagement stats for a specific request_id: impressions, opens, clicks, CTR, and per-URL click breakdown.

Args:

  • request_id: From a prior line_send_message return value.

  • oa: optional OA id.

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

Returns (structured): { request_id, delivered?: number, unique_impression?: number, // null if below ~20-user privacy floor unique_click?: number, ctr_percent?: number, per_url_clicks: [{ url, click, unique_click }], notes: string[] // T-1 lag note, privacy-floor note }

Important caveats (surfaced in notes):

  • 24-hour data lag — stats stabilize ~T+1 after send.

  • Below ~20 unique users, LINE returns null for privacy.

  • Available only for narrowcast / multicast / broadcast (not reply / push to single user).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
request_idYesrequest_id returned by a prior line_send_message (narrowcast/multicast/broadcast).
oaNo
response_formatNomarkdown
Behavior5/5

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

The description adds significant behavioral details beyond annotations: 24-hour data lag, privacy floor for small audiences, and specific message type constraints. This aligns with the readOnlyHint and idempotentHint annotations, providing full transparency.

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 (Args, Returns, Important caveats). It is front-loaded with the main purpose, and every sentence provides necessary information without redundancy.

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 tool's complexity (3 parameters, no output schema), the description is complete: it explains the return structure inline, covers caveats, and provides all needed context for an agent to use the tool correctly.

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?

Despite only 33% schema description coverage, the description thoroughly explains each parameter: request_id (source), oa (optional), response_format (enum with defaults). It also details the return structure, compensating for the lack of output 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 it fetches engagement stats for a broadcast by request_id, using a specific verb 'Fetch' and resource 'per-broadcast engagement stats'. This distinguishes it from sibling tools like line_get_coupon_stats or line_get_oa_report, which focus on different statistics.

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 specifies that the tool is available only for narrowcast/multicast/broadcast and not for reply/push, and that request_id comes from line_send_message. This provides context on when to use it, though it could explicitly mention when not to use it or suggest alternatives.

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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