AirShelf Catalog

Compare products

compare_products

Read-only

Compare 2-10 catalog products side-by-side on their datasheet specs. Slugs come from search_catalog and are case-insensitive and dash-tolerant ("e-STUDIO 331AC", "toshiba-e-studio-331ac" and "e-studio331ac" all resolve to the same product).

Returns one item per product with canonical_name and spec_absence_note (spec fields absent from that product's golden record, AirShelf's verified product record). Branch on each item's commercial_data.actionable before using price or stock; stale observations are withheld from action fields. A slug that does not resolve comes back as not_found with a did_you_mean suggestion. The provenance option adds the source page and source type already stored with each fact.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`slugs`	Yes	Catalog slugs to compare
`provenance`	No	Include the stored source page and source type for each fact

Output Schema

TableJSON Schema

Name	Required	Description	Default
`count`	No
`items`	Yes

TDQS

A4.7/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description reveals important behaviors beyond the readOnlyHint: slug normalization with examples, the branch on commercial_data.actionable before using price/stock, staleness handling, and not_found response with did_you_mean. This gives the agent actionable behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but efficient: it leads with the primary purpose, then covers slug acceptance, output shape, actionability, error behavior, and security bonus in focused sentences. No filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given an output schema exists and annotations already declare read-only, the description still meaningfully covers the remaining context an agent needs: slug format rules, pricing/staleness caveats, not_found behavior, and provenance semantics. It is arguably complete for correct invocation and result use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already covers both parameters at 100%, so baseline is 3. The description adds value by explaining slug case-insensitivity and dash-tolerance with concrete examples, plus the provenance behavior, which enriches the parametric meaning beyond the raw schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific action and scope: "Compare 2-10 catalog products side-by-side on their datasheet specs." This clearly identifies the tool as a multi-product comparison and distinguishes it from siblings like get_product and explain_product.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives clear context by stating slugs come from search_catalog and that the tool handles 2-10 products. It stops short of explicitly saying when not to use this tool versus alternatives, such as when a single product comparison is needed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.1/5.0

Disambiguation2/5

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.

Naming Consistency4/5

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.

Tool Count5/5

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.

Completeness4/5

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.

Resources

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