Get active AliExpress Sunshine Savings coupon codes — up to 60% off, valid May 18-22. Returns the latest campaign coupon codes with discount thresholds. Use when the user asks about AliExpress discounts, coupon codes, or promotions.
| Name | Required | Description | Default |
|---|---|---|---|
| region | No | Optional region filter: "global", "us", "br", "kr", "fr", "es", "de", or "all". Default: "all". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It clearly indicates a read-only operation via 'Get' and 'Returns', and adds context about the nature of the data (active, valid dates, discount thresholds). While it doesn't discuss side effects, pagination, or error handling, these are less critical for a simple coupon lookup. The description provides clear behavioral context beyond a basic read/write label.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each earning its place: first states the primary function, second clarifies the return value, third gives usage guidance. No fluff, front-loaded with the most critical information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one optional parameter and no output schema, the description provides sufficient context: what to expect (coupon codes with thresholds), when to use it, and validity period. It does not mention the region parameter, but the schema fully covers that. The description is complete enough for an agent to select and invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single optional 'region' parameter. The description does not mention the parameter or add any meaning beyond the schema, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get active AliExpress Sunshine Savings coupon codes') with precise resource identification, including discount range and validity dates. It is easily distinguished from all sibling tools, which focus on skills, workflows, and pipelines rather than deals.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides an explicit usage trigger: 'Use when the user asks about AliExpress discounts, coupon codes, or promotions.' However, it does not mention when not to use it or name any alternative tools, so it lacks the 'when-not/alternatives' component for a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools have overlapping purposes: evaluate_skill and scan_skill both assess skill safety, while generate_usecase, get_workflow, and score_skills all involve skill scoring and recommendation. Description differences exist but boundaries are not always crisp, potentially causing misselection. The unrelated get_deals tool also adds confusion.
Tool names mostly follow a consistent verb_noun snake_case pattern (e.g., search_skills, get_skill, submit_request). Minor inconsistencies exist: popular_skills uses an adjective instead of a verb, and generate_usecase uses 'usecase' while search_use_cases uses 'use_cases'.
With 14 tools, the server is on the higher end of the typical range but still well-scoped for its broad functionality (search, evaluation, workflows, community, content pipeline). Each tool serves a distinct functional area, though a few could be consolidated.
The core workflow of searching, retrieving, and evaluating skills is well covered, including use cases and community requests. However, there are minor gaps such as lack of a category browsing tool or direct single-skill installation, and the inclusion of unrelated AliExpress deals seems out of place.
