explain_productExplain the evidence behind a catalog row. Returns stored processing stages when available and always returns a golden-record field summary with source coverage.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Composite slug to explain |
| Name | Required | Description | Default |
|---|---|---|---|
| sku | No | ||
| note | No | ||
| slug | No | ||
| count | No | ||
| trace | No | ||
| record | No | ||
| category | No | ||
| trace_status | No | ||
| canonical_name | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral detail beyond annotations: it discloses that processing stages are returned only 'when available' while the golden-record field summary is always returned, setting accurate expectations about conditional output.
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 with no filler. The core purpose is front-loaded, and the second sentence efficiently explains return behavior and guarantees. Every word earns 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 read-only tool with a full output schema, the description is complete. It explains what the tool does, what it returns, and the conditional nature of part of that return, leaving no critical gap for an agent to call it 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% and the single parameter 'slug' is documented as a 'Composite slug to explain.' The description does not add further detail about slug format or construction, but with full schema coverage, 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 states a specific verb ('explain') and resource ('evidence behind a catalog row'), and clarifies the unique value proposition: stored processing stages plus a golden-record field summary with source coverage. This clearly distinguishes it from siblings like get_product, which would return product data rather than provenance.
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 when to use the tool: when you need the evidence or provenance behind a catalog record. However, it does not explicitly state when not to use it or name alternatives among the sibling tools, so the agent must infer the appropriate context.
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.
The set contains at least two genuinely confusable pairs: find_products vs research_shopping (explicitly cross-referencing each other in their own descriptions, with near-synonymous purposes of discovering products), and get_product vs explain_product vs compare_products, all of which return product records with prices and specs. The descriptions go to great lengths to disambiguate, which flags the very real boundary problem — an agent would struggle to pick correctly without reading thousands of characters.
All eight names follow a clear verb_noun snake_case pattern (compare_products, get_product, search_catalog, request_quote, research_shopping). Minor deviations: singular/plural inconsistency across pairs (get_product vs compare_products), and research_shopping reads as a noun phrase rather than verb+object. Otherwise, predictable and clean.
Eight tools is right in the sweet spot for a research/quoting workflow, and each earns its place: search, find, get, compare, explain, merchants, quote, and the heavy research job. The surface is neither bloated with near-duplicate operations nor so thin that the server feels like a stub.
The buyer-to-quote lifecycle is covered end to end: discovery (search_catalog, find_products), deep verification (get_product, explain_product, tier3 live lookup), comparison, merchant lookup, and a double opt-in request_quote flow that closes the loop. Minor gaps: no way to list prior quotes placed or track a quote's status after the initial response, and no explicit search-catalog pagination control, but agents can work around these.