find_alternativesGiven a product, return other products in the SAME category from the catalog, listed neutrally (alphabetical, never ranked). This is discovery, not a recommendation — the personal best-fit and price-tiered alternatives live in the app.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The product to find alternatives for. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that results are alphabetical and never ranked, and frames it as discovery. However, it does not mention authentication, rate limits, or behavior on missing products.
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?
The description is two sentences with no filler. It front-loads the core purpose and then adds important behavioral context, making every sentence earn its place.
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 single-parameter tool with no output schema, the description is mostly complete. It explains the method (alphabetical), scope (same category), and what it is not. It could mention case sensitivity or behavior when the product is not found, but overall it is sufficient.
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%, so the description need not add much. The description does not elaborate on the query parameter beyond the schema's description, so it meets the baseline.
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 tool returns other products in the same category, sorted alphabetically. It explicitly distinguishes itself from recommendation tools, making its purpose unambiguous.
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 indicates this is for neutral discovery, not recommendations, and mentions that personalized alternatives are elsewhere. While it does not explicitly state when not to use it, the context and sibling tool names help guide appropriate use.
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 clearly distinct purpose: find_alternatives discovers same-category products, list_brands lists brands with counts, lookup_product returns detailed facts for a specific product, and search_products allows multi-criteria search. No overlap or ambiguity.
All tool names follow a consistent verb_noun pattern (find_alternatives, list_brands, lookup_product, search_products), making the set predictable and easy to navigate.
With 4 tools, the set is appropriately scoped for a neutral reference server, covering the core operations: search, listing, lookup, and alternatives. It is on the smaller side but fits the limited domain.
The tool surface covers key use cases for a neutral product reference: search, detailed lookup, brand listing, and alternative discovery. A minor gap is the lack of a tool to list all products or categories directly, though search can compensate.