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Glama

Dummyjson

by io.github.pipeworx-io

Product

product
Read-onlyIdempotent

Fetch a single fake product by numeric id from DummyJSON. Returns name, description, price, discount, brand, category, stock, images, and rating.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoProduct ID
brandNoBrand name
priceNoProduct price
stockNoStock quantity
titleNoProduct title
imagesNoProduct images
ratingNoProduct rating
categoryNoProduct category
thumbnailNoThumbnail URL
descriptionNoProduct description
discountPercentageNoDiscount percentage

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "id": 1
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "brand": {
+      "description": "Brand name",
+      "type": "string"
+    },
+    "category": {
+      "description": "Product category",
+      "type": "string"
+    },
+    "description": {
+      "description": "Product description",
+      "type": "string"
+    },
+    "discountPercentage": {
+      "description": "Discount percentage",
+      "type": "number"
+    },
+    "id": {
+      "description": "Product ID",
+      "type": "number"
+    },
+    "images": {
+      "description": "Product images",
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "price": {
+      "description": "Product price",
+      "type": "number"
+    },
+    "rating": {
+      "description": "Product rating",
+      "type": "number"
+    },
+    "stock": {
+      "description": "Stock quantity",
+      "type": "number"
+    },
+    "thumbnail": {
+      "description": "Thumbnail URL",
+      "type": "string"
+    },
+    "title": {
+      "description": "Product title",
+      "type": "string"
+    }
+  },
+  "type": "object"
+}
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive nature. The description adds context about the data source (DummyJSON) and the exact return fields, which is supplementary but not essential given the annotations.

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?

Single sentence, front-loaded with key information, no wasted words. Efficiently conveys purpose and scope.

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?

For a simple tool with one parameter and an output schema, the description covers all necessary aspects: purpose, source, and return fields. No gaps remain.

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

Parameters2/5

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

The schema has 0% description coverage for the 'id' parameter. The description merely says 'by numeric id', which repeats the schema type. It does not add details like valid range, format, or constraints. For a single parameter, this is insufficient compensation.

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?

Description clearly states 'Fetch a single fake product by numeric id', identifying the verb, resource, and source (DummyJSON). It lists return fields, distinguishing from sibling tools like 'products' (list) and 'product_search' (search).

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?

The description implies use when you have a specific product ID, but does not explicitly say when not to use it (e.g., for searching or listing). The specificity of 'single' and 'by numeric id' provides adequate guidance.

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

All Dummyjson toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Many tools have overlapping purposes, e.g., multiple tools for data retrieval (ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile) that differ only in nuance, and the inclusion of both DummyJSON and Pipeworx tools creates confusion about which domain to use for what. Agents will struggle to select the correct tool.

Naming Consistency2/5

Naming conventions are mixed: Pipeworx tools use diverse patterns (verb_noun like 'validate_claim', noun like 'entity_profile', verb like 'forget'), while DummyJSON tools use simple nouns (posts, comments). No consistent pattern across the set.

Tool Count2/5

43 tools is excessive for a server named 'Dummyjson'. The majority are Pipeworx tools unrelated to fake data, making the set feel bloated and unfocused. The count is too large for the apparent scope.

Completeness3/5

For a fake data API, the set is incomplete: it only provides read operations (fetch, search) with no create, update, or delete capabilities. However, for the Pipeworx portion, the read coverage is extensive, so it's not severely lacking overall.

Resources