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by com.timzinin.api

Shopify Price Change Monitor

shopify-price-change-monitor

Watches a Shopify store's catalogue and returns only the products whose price or availability changed since the previous check. The first run creates the baseline and says so. — $0.05/call, x402 (USDC on base).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
websitesYesShopify (or possibly-Shopify) store URLs to watch, e.g. "allbirds.com". Every scheduled run re-checks these same sites and reports ONLY what changed since a previous run of this watch: a store newly added to the watch, or an already-tracked store's price range / catalog size / estimated revenue band shifting.
max_itemsNoCaps how many new-store / changed-store rows a single run will deliver and charge for, even if more were found.
baseline_keyNoA name for THIS watch, so you can run several independent store watches from one Actor (e.g. "dtc-competitors", "my-portfolio") without one overwriting another's memory of what's already been seen. Each name is scoped to YOUR OWN Apify account. The prefilled value is only there so this Actor's own daily test run has a stable, obviously-a-test name; replace it with your own watch name.

TDQS

A4.2/5.0
Behavior4/5

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

The description adds meaningful behavioral details beyond annotations: it mentions baseline creation on first run, the cost ($0.05/call), and the USDC on base payment. This is useful context not present in the annotations (readOnlyHint=false, openWorldHint=true). No contradiction found.

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 two sentences, front-loaded with the core purpose, followed by the baseline note and pricing. Every phrase earns its place; no redundant or filler content.

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

Completeness4/5

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

For a tool with moderate complexity, the description adequately covers the change-detection flow, baseline behavior, and cost. While no output schema exists, the core behavior is explained clearly; edge cases like store removal are not covered but are not essential for basic use.

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

Parameters3/5

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

The input schema's descriptions already cover all three parameters thoroughly (websites, max_items, baseline_key), so the tool description does not need to repeat them. The description adds no extra parameter-level semantics beyond what the schema provides.

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 states a specific verb ('Watches') and resource ('Shopify store's catalogue'), and precisely defines the output ('only the products whose price or availability changed'). It clearly distinguishes from siblings like 'shopify-store-intelligence' or 'pricing_info' by focusing on change detection over time.

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 provides clear context for use (monitoring changes since the previous check), but does not explicitly name alternatives or state when not to use it. It implies a recurring-watch scenario without excluding other monitoring tools.

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

A3.9/5.0
Disambiguation2/5

There are multiple overlapping tool pairs: tech-stack-detector vs tech-stack-change-detector (the latter is a superset that includes detection), and url-to-markdown vs structured-extract vs sitemap-to-knowledge (all fetch web pages and convert content, differing only in output format). These boundaries are unclear, and an agent could easily select the wrong one without deep reading of descriptions.

Naming Consistency2/5

Naming is a mix of hyphenated descriptors (domain-health-checker, tech-stack-detector), snake_case (pricing_info), and verb phrases (structured-extract, url-to-markdown). The pattern is inconsistent: some tools are named after the action (extract, convert), others after the target (shopify-store-intelligence). This makes it hard to predict tool names.

Tool Count5/5

With 10 tools, the count is well within the 3-15 ideal range. Each tool addresses a distinct web intelligence need (domain health, tech stack, e-commerce, content extraction, pricing), and none are purely redundant filler. The scale feels appropriate for the server's stated purpose.

Completeness4/5

The surface covers core web intelligence workflows well: domain auditing, tech stack detection (with change detection), content extraction, and e-commerce monitoring for Shopify and Zid. Minor gaps exist, such as missing generic e-commerce platform coverage or a dedicated WHOIS lookup, but these are not critical given the existing domain-health-checker.

Resources