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AirShelf Catalog

by ai.airshelf
OverviewDiscussionsAdmin

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
NameRequiredDescriptionDefault
slugsYesCatalog slugs to compare
provenanceNoInclude the stored source page and source type for each fact

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNo
itemsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / provenance / description
      Previous value: -"Include the source page for each fact plus a signed-attestation URL"New value: +"Include the stored source page and source type for each fact"
    • addedInput schema / properties / slugs / items / minLength
      Added value: +1
  2. Changed1 schema field changed
    • changedInput schema / properties / provenance / description
      Previous value: -"Include per-claim source provenance + signed-attestation URL + jwks_url"New value: +"Include the source page for each fact plus a signed-attestation URL"
  3. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": false,
+  "properties": {
+    "count": {
+      "type": "number"
+    },
+    "items": {
+      "items": {
+        "additionalProperties": {},
+        "properties": {
+          "canonical_name": {
+            "type": "string"
+          },
+          "manufacturer": {
+            "type": "string"
+          },
+          "modelName": {
+            "type": "string"
+          },
+          "price": {
+            "anyOf": [
+              {
+                "type": "number"
+              },
+              {
+                "type": "null"
+              }
+            ]
+          },
+          "slug": {
+            "type": "string"
+          },
+          "spec_absence_note": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ]
+          }
+        },
+        "required": [
+          "slug"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "items"
+  ],
+  "type": "object"
+}
  4. Changed1 schema field changed
    • addedInput schema / properties / provenance
      Added value: +{
+  "description": "Include per-claim source provenance + signed-attestation URL + jwks_url",
+  "type": "boolean"
+}
  5. First observed

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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