lookup_food_barcode
用商品條碼查食品的成分、營養、過敏原（Open Food Facts 全球食品資料庫）。使用者掃到條碼、問食品成分/營養時使用。
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| barcode | Yes | 商品條碼（EAN/UPC 數字） |
ecpro-mcp
TDQS
A4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It names the database (Open Food Facts) and implies it is a read operation, but does not disclose behavior on invalid barcodes, rate limits, or whether it requires authentication. Minimal but adequate.
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 compact sentences, no filler. All information is front-loaded and relevant.
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?
For a simple lookup tool with one parameter and no output schema, the description does well: it lists the data returned (ingredients, nutrition, allergens) and the database. Could mention handling of unknown barcodes or response format, but fine overall.
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 coverage is 100% with one parameter 'barcode' described as '商品條碼（EAN/UPC 數字）'. The tool description adds context about using barcode to query food data, which overlaps with the schema description. Baseline score of 3 is appropriate.
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?
Description clearly states the tool uses a barcode to look up food ingredients, nutrition, and allergens from Open Food Facts. It provides a specific verb-resource pair and distinguishes itself from sibling tools focused on e-commerce and pricing.
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?
Description explicitly says when to use: when user scans a barcode or asks about food ingredients/nutrition. It does not state when not to use or list alternatives, but the context is clear enough.
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.