get_ecommerce_article
用 slug 取得 ECPRO 電商博士單篇文章的完整內容（純文字），含常見問答（FAQ）。先用 search_ecommerce_articles 找到 slug 再呼叫。
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | 文章 slug，例如 aov-average-order-value-optimization |
ecpro-mcp
TDQS
A4.2/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that the tool returns plain text content including FAQ, but does not mention authentication needs, rate limits, or any side effects. For a simple read operation, this is adequate but not comprehensive.
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?
Two sentences with no wasted words. Front-loaded with the action and key details: what it retrieves (content, FAQ) and how to use (find slug first). Efficient and scannable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple tool with one parameter and no output schema, the description covers the essential workflow and output. It could mention potential error cases or response format details, but is sufficiently complete for an AI agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explains the purpose of the slug parameter (to identify the article found via search) and provides an example value. Schema coverage is 100%, but the description adds contextual usage guidance beyond the schema's basic description.
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 retrieves the complete plain text content of a single ECPRO article by slug, including FAQ. It distinguishes itself from sibling tool search_ecommerce_articles by specifying that the slug must be obtained from that search first.
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?
Explicitly instructs to first use search_ecommerce_articles to find the slug before calling this tool. Provides clear prerequisite context, though it does not elaborate on when not to use 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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TDQS
A3.7/5.0
Disambiguation4/5
Most tools have clearly distinct purposes, such as price comparison vs. price history vs. store safety. However, there is some overlap between compare_prices, cross_channel_gaps, and price_match_check, which all involve comparing prices across channels. The descriptions help differentiate them, but ambiguity remains for agents.
Naming Consistency3/5
Tool names use snake_case but vary in structure: some start with verbs (compare_prices, find_deals), others with nouns (category_price_range, price_match_check). There is no strict verb_noun pattern, and 'should_i_buy_now' is a full phrase, breaking consistency.
Tool Count4/5
17 tools cover a wide range of e-commerce assistance tasks without being overwhelming. The count is appropriate for the domain, though a few tools (e.g., price_match_check vs. should_i_buy_now) could potentially be merged without losing functionality.
Completeness4/5
The tool set covers most common user needs in Taiwanese e-commerce: product search, price comparison, historical trends, deals, store safety, tech detection, and currency conversion. Missing operations include user-specific features (e.g., watchlist) but the core lifecycle is well-represented.