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tzangms

shopline-mcp

by tzangms

list_channels

Retrieve the enabled sales channels in your Shopline store, covering online store, POS, and social platforms. Verify channel configuration or prepare for multi-channel performance analysis.

Instructions

取得商店所有銷售渠道清單。

【用途】 查看商店目前開啟的銷售渠道,例如線上商店、POS、 Facebook、Instagram 等。適合了解多渠道銷售佈局, 或做渠道業績分析前的渠道資料確認。

注意:此端點在部分 token 權限下可能回傳 403, 渠道資訊亦可從訂單的 channel.created_by_channel_name 欄位取得。

【呼叫的 Shopline API】

  • GET /v1/channels?platform={platform}

【回傳結構】 dict 含 total, platforms_queried[], platforms_failed[], channels[]。 每個 channel 包含 id, name, channel_type, platform, enabled, created_at 等。 platforms_failed 非空代表部分平台查詢失敗,此時 channels 不完整 (勿將空結果直接解讀為「沒有渠道」)。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
platformNo平台篩選,如 shopline / shopline_pos。不填則自動查詢常見平台並合併結果
Behavior5/5

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

The description goes beyond the basic function by disclosing potential 403 errors under certain token permissions, and explains that platforms_failed indicates partial failures, warning against interpreting empty results as 'no channels'. This is valuable behavioral context, especially with no annotations provided.

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 sections for purpose, usage, API endpoint, and return structure. Each section provides essential information without unnecessary fluff, making it easy for an agent to quickly 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?

Given a single optional parameter, no annotations, and no output schema, the description is remarkably complete. It explains the return structure, fields, failure modes, and caveats, giving the agent all necessary context to use the tool correctly and interpret results.

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?

The only parameter `platform` is fully documented in the schema with examples and default behavior (auto-query common platforms and merge results). The description adds no extra semantic value beyond repeating the schema, so a baseline score of 3 is appropriate for full 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 it retrieves the list of all sales channels (取得商店所有銷售渠道清單) with examples like online store, POS, Facebook, Instagram. It is clear about the resource and action, but does not explicitly differentiate from sibling tools like get_channel_detail, though the 'list' vs 'detail' distinction is implied.

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 use cases: '適合了解多渠道銷售佈局,或做渠道業績分析前的渠道資料確認' (suitable for understanding multi-channel layout or confirming channel data before performance analysis). It does not mention alternative tools to use instead, but gives clear contextual guidance on when to invoke this tool.

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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