Agent Data API

Shopify store technographics

shopify_lookup

Read-onlyIdempotent

action=store: one Shopify store's apps by category (reviews, email, subscriptions, loyalty...), pixels, theme and catalog hints, gaps and sales pitch angles. action=search: stores from our index of ~19k Shopify stores by app used / not used, missing category, country and keyword (lead lists). Price: store $0.02, search $0.05. Store reads are live (3-15 s); the index is refreshed weekly. Paid with x402 (USDC on Base, Polygon or Solana) per call; failed or empty calls are never charged.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "action": "<action>"
    },
    "name": "shopify_lookup"
  }
}

Input Schema

Name	Required	Description
`q`	No	search: keyword in the store name or products.
`app`	No	search: stores using this app, e.g. Klaviyo.
`limit`	No	search: rows (default 10).
`action`	Yes	store = one store by domain; search = find stores in the index.
`domain`	No	store: the store's domain, e.g. allbirds.com.
`country`	No	search: 2-letter country, e.g. US.
`missing`	No	search: stores with no app in this category, e.g. loyalty.
`exclude_app`	No	search: stores NOT using this app.

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 24, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Beyond annotations (readOnly, idempotent, non-destructive), the description discloses critical behavioral traits: pricing per call ($0.02 store, $0.05 search), latency (live store reads 3–15s, index refreshed weekly), payment method (x402 USDC on Base, Polygon, or Solana), and billing guarantee that failed/empty calls are never charged. This is exactly the kind of operational context an agent needs.

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 front-loaded with action definitions, then layers on pricing, latency, and payment details in a logical sequence. Every sentence contributes unique, high-value information; there is no redundancy or 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 two action modes, eight parameters, and no output schema, the description covers everything needed to select and invoke the tool correctly: what each action returns, cost per call, latency expectations, payment mechanics, and billing fairness. Output format is unspecified, but the description conveys the content of returned data sufficiently for an agent's purposes.

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 coverage is 100%, so all parameters are already documented. The description adds some context around the action enum (what each action returns) but does not introduce new constraints, formats, or semantic nuances for parameters like 'app', 'missing', or 'exclude_app' beyond what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.

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 specifies a clear verb+resource for each action: 'store' returns one Shopify store's apps, pixels, theme, and gaps; 'search' finds stores from an index of ~19k by app usage, missing category, country, and keyword. It distinguishes the two modes explicitly and gives concrete output examples, leaving no ambiguity about what the tool does.

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 clearly states the scenario for each action: 'action=store: one Shopify store's...' and 'action=search: stores from our index...'. The use case for search is further clarified as 'lead lists'. No explicit when-not-to-use is provided, but with no similar sibling tools, alternatives are unnecessary.

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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