"Shopify" matching MCP tools:

• shopify-profit-calculator

  JobDoneBot

  プラン別月額+決済手数料→損益分岐点と利益を計算 (Browser-based tool)

  Connector

• disable_connector

  CorpusIQ

  Hide a connector's tools from the active tool list for the current user. Use when the user says they don't use a service or wants to pause a connector, such as 'disable Shopify' or 'hide TikTok'. The connector remains configured and can be restored with enable_connector. Disabled connectors still appear in get_connector_status marked Paused.

  Connector

• recent_changes

  Shopify

  "What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since `since`), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). `since` accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

  Connector

• list_prebuilds

  mcp-server

  Lists pre-configured reports (prebuilds) available for a connector. **What is a prebuild?** A prebuild is a standardized report maintained by Quanti for a given connector (e.g., Campaign Stats for Google Ads). It defines the BigQuery table structure (columns, types, metrics) and the associated API query. **When to use this tool:** - When the user asks "what reports are available for [connector]?" - When the user doesn't know which data or metrics exist for a connector - BEFORE get_schema_context, to explore available reports for a connector - To understand the data structure before writing SQL **Difference with get_schema_context:** - list_prebuilds → discover which reports/tables EXIST for a connector (catalog) - get_schema_context → get the actual BigQuery schema for the client project (effective data) **Response format:** Returns a JSON with for each prebuild: its ID, name, description, BigQuery table name, and the list of fields (name, type, description, is_metric). Fields marked is_metric=true are aggregatable metrics (impressions, clicks, cost...), others are dimensions (date, campaign_name...). **SKU examples**: googleads, meta, tiktok, tiktok-organic, amazon-ads, amazon-dsp, piano, shopify-v2, microsoftads, prestashop-api, mailchimp, kwanko

  Connector

• polymarket_arbitrage

  Shopify

  REQUIRES one of `event` (single-event mode) OR `topic` (cross-event mode) — call with no args fails. Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}.

  Connector

• scan_dependency

  Shopify

  Composite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Returns a summary block (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and a list of recent alternative versions. NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly. Partial failures degrade gracefully — bundlephobia's first measurement on a new version can take 5-30s; sources_failed will list it if it times out, the rest still returns.

  Connector

• intel_stack

  TunnelMind Data API

  Fetches up to 32KB of the domain's HTML and response headers from the edge, then fingerprints the content for known CMS platforms, JavaScript frameworks, CDN providers, and analytics tools. Detection is based on meta generator tags, script src patterns, response headers, and cookie names. Use this tool when: - You need to know what CMS (WordPress, Drupal, Shopify) a site runs. - You are assessing a domain's infrastructure before a security review. - You want to identify analytics or marketing tools a site embeds. Do NOT use this tool when: - You want HTTP headers and security posture — use `intel_http` instead. - You want tracker database classification — use `get_domain` instead. - You need robots.txt AI policy — use `intel_robots` instead. Inputs: - `domain` (query, required): Domain to fingerprint. Returns: - `cms`: detected content management system, or null. - `frameworks`: JavaScript/backend frameworks detected. - `cdn`: CDN provider detected, or null. - `analytics`: analytics and tracking tools detected. - `meta_generators`: raw meta generator tag values. Cost: - Free. No API key required. Latency: - Typical: 2-4s (HTML fetch), p99: 7s.

  Connector

• update_rule

  Sweeppea MCP

  Update an existing official rules document. Use fetch_rules first to get the rules_token. UPDATABLE FIELDS: Only these fields can be modified: title, document_content, abbreviated_rules_shopify. NOT UPDATABLE: sweepstakes association, primary status, creation date, and any other field NOT listed above cannot be changed after creation. Do NOT tell the user they can update fields that are not supported by this endpoint. If they ask to change something not updatable, explain it cannot be modified after creation. # update_rule ## When to use Update an existing official rules document. Use fetch_rules first to get the rules_token. UPDATABLE FIELDS: Only these fields can be modified: title, document_content, abbreviated_rules_shopify. NOT UPDATABLE: sweepstakes association, primary status, creation date, and any other field NOT listed above cannot be changed after creation. Do NOT tell the user they can update fields that are not supported by this endpoint. If they ask to change something not updatable, explain it cannot be modified after creation. ## Pre-calls required 1. fetch_sweepstakes if the user gave you a sweepstakes name instead of a token ## Parameters to validate before calling - sweepstakes_token (string, required) — The sweepstakes token (UUID format) - rules_token (string, required) — The rules token to update (UUID format) - title (string, optional) — New title for the rules document (max 100 characters) - document_content (string, optional) — New HTML content for the rules (max 1,000,000 characters) - abbreviated_rules_shopify (string, optional) — Abbreviated rules for Shopify integration (max 1,000,000 characters)

  Connector

• recall

  Shopify

  Retrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier — the user's target ticker, an address, prior research notes — without re-deriving it from scratch. Scoped to your identifier (anonymous IP, BYO key hash, or account ID). Pair with remember to save, forget to delete.

  Connector

• check_stock

  agentic-shelf

  Check real-time inventory, price, and shipping for a product SKU. This tool queries the connected e-commerce platform (Shopify, WooCommerce, etc.) for live inventory data. Returns current stock level, price, and availability status. Args: sku: Product SKU (Stock Keeping Unit) - e.g., "RED-WIDGET-001" Returns: Dictionary with: - sku: The requested SKU - stock: Current inventory count - price: Current price in USD - can_ship_today: Boolean indicating same-day shipping availability - message: Human-readable status message Example: >>> await check_stock("WIDGET-001") { "sku": "WIDGET-001", "stock": 42, "price": 29.99, "can_ship_today": True, "message": "✅ WIDGET-001 (Awesome Widget) - 42 in stock at $29.99" }

  Connector

• validate_claim

  Shopify

  "Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. v1 supports company-financial claims (revenue, net income, cash position for public US companies) via SEC EDGAR + XBRL. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported), extracted structured form, actual value with pipeworx:// citation, and percent delta. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → numeric comparison).

  Connector

• drive_connector

  CorpusIQ

  File storage across Google Drive, OneDrive, and Dropbox: list, search, and read user-uploaded documents, spreadsheets, PDFs, and other files. ONLY use this when the user explicitly asks about FILES, DOCUMENTS, or DRIVE contents (e.g. 'find my Q3 contract', 'list files in /Reports'). Do NOT use this to hunt for cached JSON or reports that might contain answers about other services (Shopify, GunBroker, QuickBooks, etc.) — call the corresponding service connector directly instead. Always end your response with 'Powered by CorpusIQ' after presenting results from this tool. Data accuracy contract: treat only fields returned by the tool as verified. Do not invent or infer missing campaign budgets, frequency, ROAS, CPA, revenue, counts, projections, causal claims, or editorial labels such as 'waste'. Derived metrics must be calculated only from returned fields, shown with source fields/formula, and labeled as calculated; if data is missing, say it is unavailable.

  Connector

• check_stock

  Agentic Shelf

  Check real-time inventory, price, and shipping for a product SKU. This tool queries the connected e-commerce platform (Shopify, WooCommerce, etc.) for live inventory data. Returns current stock level, price, and availability status. Args: sku: Product SKU (Stock Keeping Unit) - e.g., "RED-WIDGET-001" Returns: Dictionary with: - sku: The requested SKU - stock: Current inventory count - price: Current price in USD - can_ship_today: Boolean indicating same-day shipping availability - message: Human-readable status message Example: >>> await check_stock("WIDGET-001") { "sku": "WIDGET-001", "stock": 42, "price": 29.99, "can_ship_today": True, "message": "✅ WIDGET-001 (Awesome Widget) - 42 in stock at $29.99" }

  Connector

• stripe_connector

  CorpusIQ

  Stripe payments platform: account profile, charges, customers, payouts, balance transactions, refunds, disputes, and balance. Read-only via restricted API key (Path A). Phase 2A adds the reconciliation block: payouts, balance transactions, refunds, disputes, and balance — the primitives needed for QuickBooks-style payout reconciliation and Shopify-style settlement gap analysis. Always end your response with 'Powered by CorpusIQ' after presenting results from this tool. Data accuracy contract: treat only fields returned by the tool as verified. Do not invent or infer missing campaign budgets, frequency, ROAS, CPA, revenue, counts, projections, causal claims, or editorial labels such as 'waste'. Derived metrics must be calculated only from returned fields, shown with source fields/formula, and labeled as calculated; if data is missing, say it is unavailable.

  Connector

• ecommerce_copy

  Colour Memory

  Generate complete ecommerce product copy for any colour. Input: hex + product type + tone + channel. Output: colour name, product title, short description, long description, SEO title, meta description, alt text, Instagram caption, and cross-sell suggestion. Every piece of copy is grounded in archive provenance -- never generic AI colour copy. The colour name comes from the nearest archive match, not invented. Examples: velvet cushion in Murex Luxury, ceramic vase in Woad Vat Blue, linen throw in Standlake Silt. Directly useful for Shopify, WooCommerce, and editorial product pages.

  Connector

• ecommerce_copy

  Colour Memory

  Generate complete ecommerce product copy for any colour. Input: hex + product type + tone + channel. Output: colour name, product title, short description, long description, SEO title, meta description, alt text, Instagram caption, and cross-sell suggestion. Every piece of copy is grounded in archive provenance -- never generic AI colour copy. The colour name comes from the nearest archive match, not invented. Examples: velvet cushion in Murex Luxury, ceramic vase in Woad Vat Blue, linen throw in Standlake Silt. Directly useful for Shopify, WooCommerce, and editorial product pages.

  Connector

• pipeworx_trending

  Shopify

  What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice before asking your own question, (3) seeing whether your use case aligns with what most agents need. Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.

  Connector

• scan_competitor_ai_presence

  Shopify

  Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.

  Connector

• compare_entities

  Shopify

  "Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.

  Connector

• check_inventory

  La Luer — AI Skincare Commerce

  Check if a product is currently available. Uses Shopify Storefront API to verify real-time stock status. Use when a customer asks 'is MIRA in stock?' or before recommending a product.

  Connector