Classify a product name into the most likely HSN code via keyword scoring. Returns {best, alternatives[], query, matched_tokens[]}. Best-effort: always returns a result (best may be null if nothing matched).
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Product / service name, e.g. 'cotton t-shirt' or 'pre-paid SIM card'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It discloses the return structure ({best, alternatives[], query, matched_tokens[]}) and the fallback behavior (best may be null). It does not mention side effects or error conditions, but for a classification tool this is adequate.
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?
Two sentences, front-loaded with purpose and return structure, no redundant information. Every word serves a purpose.
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 one-parameter tool without output schema, the description covers purpose, return structure, and edge case behavior. Missing explicit comparison with sibling tools and error handling, but overall complete enough.
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 coverage is 100% with a clear description of the 'name' parameter. The description adds context about classification into HSN codes, but does not provide additional value beyond what the schema already conveys.
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 verb (classify), resource (product name into HSN code), and method (keyword scoring). It distinguishes from sibling tools (lookup_hsn and search_hsn) by focusing on classification of product names.
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 when a product name needs an HSN code, but it does not explicitly state when to use this tool over siblings or provide exclusions. The 'best-effort' note provides some behavioral guidance but not comparative.
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.
Each tool has a distinct input and output: classify_product takes a product name and returns HSN codes; lookup_hsn takes an HSN code and returns description; search_hsn takes keywords and returns matching HSN descriptions. There is no overlap in purpose.
All tool names follow a consistent verb_noun pattern: classify_product, lookup_hsn, search_hsn. The verbs are distinct (classify, lookup, search) and the nouns correspond to the entity (product, hsn).
Three tools is appropriate for a specialized HSN classifier server. It covers the essential operations: classifying a product, looking up a code, and searching by keyword. Not too many or too few.
The tool set covers the main use cases (classify, lookup, search) but could benefit from a tool to list all HSN sections or chapters for hierarchical browsing. However, for the stated purpose of product classification, it is largely complete.