Classify product descriptions to HS (Harmonized System) customs codes using AI. Returns code, chapter, confidence, and reasoning.
Scored across 1 tool
With only one tool, there is no possibility of confusion or overlap with other tools.
The single tool name follows a clear verb_noun pattern (classify_product), which is consistent and descriptive.
The server has only one tool, which is a borderline thin surface for a classifier service; while it fully addresses the core need, additional tools (e.g., batch classification or code lookup) might be expected.
The tool covers a single classification operation, but lacks lifecycle operations such as retrieving code details, validating codes, or handling multiple products; notable gaps exist for a classification service.
Given a product description (e.g., 'wireless bluetooth headphones'), returns the most likely HS (Harmonized System) customs code with confidence score and reasoning.
| Name | Required | Description | Default |
|---|---|---|---|
| description | Yes | Product description in English |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does disclose that the tool returns a confidence score and reasoning, which is useful behavioral context. However, it says nothing about accuracy limits, how ambiguous products (which don't map cleanly to one code) are handled, or whether the result is deterministic/cached.
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?
A single tight sentence that is front-loaded with the input and states the output. Efficient and free of filler, though it packs input, output, and return fields into one sentence rather than structuring them.
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 one-parameter, no-output-schema tool, the description is largely self-sufficient: it explains the input, the result type (HS code), and the supplementary return fields (confidence, reasoning). The main remaining gap is uncertainty/ambiguity handling, which is minor at this complexity.
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?
Only one parameter, and schema coverage is 100% — the schema already documents it as 'Product description in English'. The description's inline example adds a concrete illustration but no syntax, length, or format constraints beyond what the schema provides. Baseline 3 is correct.
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?
States a specific verb (classify) and resource (product to HS customs code), and the example input makes the operation unambiguous. No siblings exist to differentiate from, but the purpose is fully clear on its own.
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?
The description implies usage via the input example ('wireless bluetooth headphones') but gives no explicit when-to-use guidance, no conditions of applicability, and no alternatives (there are no siblings to name). Adequate but with a clear gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
classify_product
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