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Web Intelligence Tools — Zinin M2M Hub

Shopify Store Intelligence

shopify-store-intelligence
Read-only

Confirm a site runs on Shopify and pull store intelligence from its public feeds — product count, price range, top vendors/categories, newest listing and a rough revenue-band heuristic. No login, no Shopify API key. — $0.01/call, x402 (USDC on base).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
websitesYesList of websites to check (e.g. `allbirds.com` or `https://example.com`). One row per site.
maxConcurrencyNoHow many websites to check in parallel.

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already provide readOnlyHint=true and destructiveHint=false. The description adds valuable non-obvious behavior: per-call cost ($0.01/call), no auth needed, reliance on public feeds, and the heuristic nature of the revenue-band estimation. It does not disclose behavior for non-Shopify or invalid sites, but the gap is small given annotation coverage.

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 a single, dense sentence that front-loads the purpose and immediately lists specific outputs, followed by crucial pricing/auth details. No word is wasted; the cost and no-key info are presented as a secondary clause without bloat.

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?

Despite lacking an output schema, the description enumerates the return fields (product count, price range, etc.). It covers cost, auth, and read-only nature. Missing edge-case details (e.g., handling of non-Shopify sites) and explicit concurrency behavior, but these are minor for a simple read-only tool with good annotations.

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?

Schema description coverage is 100% for both parameters (websites and maxConcurrency), including formats and defaults. The description adds no parameter-specific context beyond the schema, so a baseline of 3 is appropriate.

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 clearly states the tool's function: confirm Shopify usage and pull store intelligence (product count, price range, vendors, etc.). It uses specific verbs ('confirm', 'pull') and a specific resource ('public feeds'), distinguishing it from siblings like the general tech-stack-detector.

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 context: it is for Shopify sites, uses public feeds, requires no login/API key, and costs money. It does not explicitly name alternatives or exclusions (e.g., 'for general stack detection, use tech-stack-detector'), but the Shopify-specific scope and output list imply when to use it.

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
Disambiguation4/5

Most tools have clear distinct purposes, but url-to-markdown and structured-extract both fetch a single URL and could be confused; tech-stack-detector and tech-stack-change-detector are closely related but descriptions clarify the difference. Overall, the tool set is well-differentiated.

Naming Consistency4/5

The majority of tools use lowercase hyphenated names (e.g., domain-health-checker, url-to-markdown), but pricing_info breaks the pattern with an underscore. There is also some variation in style (detector vs. to-knowledge vs. extract), but it remains readable and predictable overall.

Tool Count5/5

Ten tools is well within the ideal range for a web intelligence bundle, covering domain health, store data, content extraction, and tech stack detection without feeling bloated or sparse. Each tool earns its place for specific use cases.

Completeness4/5

The surface covers major web intelligence needs: domain audits, Shopify/Zid store data, content fetching/transformation, and tech stack detection. Minor gaps exist, such as no generic price-change monitor for non-Shopify stores and no whois/backlink tools, but these are not critical for the stated purpose.

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