AgentDiet Shopify & DTC E-Commerce Store Competitive Intelligence

extract_shopify_store_intel

Read-onlyIdempotent

Extracts live product catalogs, pricing tiers, SKU variants, and stock availability from any Shopify or e-commerce storefront. WHEN TO USE: Call extract_shopify_store_intel when your task requires deterministic shopify-intel analysis for store_url; use get_agent_wallet_status to check your remaining free trial quota or prepaid balance. WHEN NOT TO USE: Do not use for unrelated domains or binary media. PARAMETER INTERACTIONS: Pass the target store_url as a non-empty string; omit agent_id and tx_hash during your 5 free trial calls, or supply agent_id (agd_pass_...) / tx_hash for paid execution. Read-only and idempotent; results are cached for 1 hour (<5ms cache hit). $0.02 per call. Pricing: the first 5 calls per agent_id are free and failed calls are never charged. After 5 calls, the tool returns a payment-required quote (not a fatal error) with a $2 Stripe top-up link and Base/Solana USDC addresses; complete payment, then retry with your agd_pass_ key in agent_id or pass tx_hash. Example: extract_shopify_store_intel(store_url='example')

Input Schema

TableJSON Schema

Name	Required	Description
`tx_hash`	No	Optional on-chain Base Mainnet (0x...) or Solana SPL USDC transaction hash. Omit unless settling an individual call directly on-chain instead of using an agd_pass_ key in agent_id.
`agent_id`	No	Optional agent identifier or prepaid agd_pass_ session key. Omit during your 5 free trial calls to use an ID automatically derived from your client IP, or pass your agd_pass_ key after topping up.
`store_url`	Yes	Target Shopify or e-commerce store URL to extract structured product and pricing intelligence.

Output Schema

TableJSON Schema

Name	Required	Description
`data`	No	Structured tool output payload.
`status`	Yes	Execution status ('ok' or 'payment_required').
`roi_proof`	No	Token compression and cost-savings verification metrics.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 19, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Goes well beyond the annotations (which only cover read-only/idempotent/open-world safety) by disclosing caching behavior (1 hour, <5ms hits), per-call cost ($0.02), a 5-call free trial, failed calls never charged, and the non-fatal payment-required quote flow with retry mechanics. This is exactly the operational context an agent needs before invoking.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the purpose, then cleanly sectioned into WHEN TO USE / WHEN NOT TO USE / PARAMETER INTERACTIONS. The pricing and payment narrative is repeated in two places (brief $0.02 line plus a longer paragraph), which is slightly redundant, keeping it from a 5.

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?

Covers purpose, routing to the sibling, the full free-trial/paid execution lifecycle, caching, and failure semantics; an output schema exists, so return-value explanation is correctly omitted. Nothing needed to invoke it correctly is missing.

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%, so the schema already documents store_url, agent_id, and tx_hash thoroughly; per the rubric the baseline is 3. The description reinforces the trial-vs-paid logic (omit agent_id/tx_hash during free calls, supply agd_pass_ or tx_hash afterward) but adds little syntax beyond what the schema fields already state.

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?

Specific verb (extracts) plus concrete resource set (product catalogs, pricing tiers, SKU variants, stock availability) and scope (any Shopify or e-commerce storefront). It also names the sibling get_agent_wallet_status as the tool for a different need, so an agent can separate the two without opening schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit WHEN TO USE and WHEN NOT TO USE sections, with a named alternative (get_agent_wallet_status) for quota checks and a stated exclusion (unrelated domains, binary media). The condition that selects this tool over the alternative is spelled out.

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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