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Decodo

by io.github.pipeworx-io

Decodo Amazon Product

decodo_amazon_product
Read-onlyIdempotent

Get structured Amazon product data via Decodo (formerly Smartproxy) — title, pricing, rating, reviews, images, availability — parsed into JSON. Pass either a full Amazon product URL or an ASIN. BYOK — _apiKey is your Decodo Web Scraping API "username:password" credentials. Example: decodo_amazon_product({ asin: "B09H74FXNW", _apiKey: "user:pass" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
geoNoProxy exit location as a location name, e.g. "United States". Optional.
urlNoFull Amazon product page URL, e.g. "https://www.amazon.com/dp/B09H74FXNW". Provide this or `asin`.
asinNoAmazon ASIN (product identifier), e.g. "B09H74FXNW". Used to build the product URL when `url` is omitted.
domainNoAmazon marketplace domain used with `asin`, e.g. "com" (default), "co.uk", "de".
_apiKeyYesDecodo Web Scraping API credentials as "username:password" (from https://dashboard.decodo.com).

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "_apiKey": "your-decodo-api-key",
+    "asin": "B09H74FXNW"
+  },
+  {
+    "_apiKey": "your-decodo-api-key",
+    "geo": "United States",
+    "url": "https://www.amazon.com/dp/B09H74FXNW"
+  }
+]
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Adds context beyond annotations: explains BYOK credential format, mentions output is JSON with specific fields. No contradictions with annotations (readOnlyHint, idempotentHint, etc.).

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?

Two sentences: first defines purpose and output, second specifies inputs with example. Front-loaded and no wasted words.

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 no output schema, description sufficiently explains return fields. Covers authentication, input options, and includes an example. Complete for a 5-parameter scraping tool.

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?

Schema has 100% description coverage, so baseline 3. Description adds value by showing example usage and emphasizing the _apiKey format, improving understanding beyond 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?

Clearly states it gets structured Amazon product data via Decodo, listing specific data fields. Distinguishes from siblings like decodo_google_search by focusing on product data from Amazon URLs/ASINs.

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?

Provides clear input options (URL or ASIN) and example, but does not explicitly differentiate from sibling tools like decodo_scrape or state when not to use.

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

A4.2/5.0
Disambiguation4/5

Most tools have distinct purposes with detailed descriptions; however, the family of ask_pipeworx tools (beta, grounded) and deep_research could cause selection ambiguity despite clear documentation.

Naming Consistency3/5

Tool names lack a consistent pattern; they mix imperatives, descriptive nouns, and domain prefixes. While overall readable, the lack of uniformity makes it harder to predict naming conventions.

Tool Count4/5

34 tools is on the higher side but still within reasonable range given the broad scope (data queries, prediction markets, scraping, subscriptions, memory). Each tool appears purposeful, though some consolidation (e.g., ask_pipeworx variants) could reduce count.

Completeness4/5

The tool set covers a wide range of tasks from data querying to prediction market analysis and entity management. Minor gaps might exist (e.g., no direct social media data), but the overall coverage is extensive and sufficient for the platform's purpose.

Resources