"How to import Shopify sales data into a database and create entries in Craft CMS" matching MCP tools:

• get_domain

  TunnelMind Data API

  Returns the complete surveillance intelligence record for a domain name. If the domain is in TunnelMind's tracker database (80,000+ entries), the response includes tracker category, risk score, fingerprinting data, cookie persistence, IAB TCF purposes, and the owning corporate entity. If the domain is not in the database, a live probe is automatically run: RDAP registration data, DNS records (MX, SPF, TXT verification tokens), HTTP headers, and CSP third-party actors are fetched fresh from the edge and returned. Use this tool when: - You need to know whether a specific domain tracks users, and how aggressively. - You are researching who owns a domain and what corporate entity controls it. - You want to check HTTP security headers and third-party services embedded in a site. - You are building a risk score for a domain before routing traffic through it. Do NOT use this tool when: - You want to search by keyword or category — use `search` instead. - You want all domains for an entity — use `get_entity` instead. Inputs: - `domain` (path, required): Domain name. Strip `www.` prefix — it is removed automatically. Subdomains are resolved to the parent: `ads.doubleclick.net` → `doubleclick.net`. Examples: `doubleclick.net`, `google-analytics.com`, `intercom.io`. Returns: - Full `DomainRecord`. Free tier returns the domain, category, score, prevalence, and entity name. Pro/enterprise additionally return `tcf_vendor_id`, `tcf_purposes`, `tcf_features`, and `disconnect_cats`. - If the domain is not in the tracker database, `live_lookup: true` is set and RDAP/DNS/HTTP probe results are returned instead of tracker fields. - 404 if the domain cannot be found via live probe either (unknown TLD, unreachable). Cost: - Free tier: included in 50 req/day limit. Pro/enterprise: included in plan. Latency: - Database hit: typical <100ms, p99 <300ms. - Live probe: typical 2-5s, p99 10s (external DNS/HTTP calls).

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• nlqdb_read

  nlqdb — analytical memory for AI agents

  Read-only natural-language query over your agent's memory — SELECT / aggregate / JOIN over existing data. Guaranteed never to write, create, or modify: a request whose plan would change data is refused (use nlqdb_query for that), so this tool is safe to mark 'always allow' in your host. Auto-targets your only database; pass `db` to pick one when you have several. Returns rows + the compiled SQL in trace.

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• create_checkout

  La Luer — AI Skincare Commerce

  Create a checkout URL for one or more products. Pass variant IDs (items) and/or product URLs (product_urls). When a product URL is provided (e.g. https://laluer.com/products/mira), the tool resolves it to a variant ID automatically — no catalog import needed. Supports discount codes, cart notes, and selling plans. Do not use unless the user wants to buy — use search_products or skincare_recommend first. Returns a direct Shopify checkout link the user can click to buy.

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• legal_list_laws

  Lawbster

  List available laws, regulations, and court decisions in the database. Returns abbreviation, title, source type, jurisdiction, document kind, and version date for each entry. Unfiltered listings can contain thousands of entries; pass a search term or source_type to keep responses focused. Useful for discovering valid law abbreviations to use as filters in legal_search. Found a relevant law? Use legal_get_toc to browse its structure. NOT an existence check for a specific law: EUR-Lex entries store the official long title, so searching by common name or number can miss laws that ARE in the corpus. To verify a law exists, use legal_lookup with a citation or legal_search with a topic instead.

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• metafield_key_check

  Moltline Shopify Prep

  Validate a metafield namespace and key before you define it. FREE. Shopify metafield namespaces and keys are lowercase alphanumerics and underscores; a capital letter or a dash is rejected at definition time, usually after the rest of the import has already run. Typical input {"namespace": "custom", "key": "Care-Instructions"} returns {"ok": false, "problems": ["key: dashes and capitals are not allowed"], "suggested_key": "care_instructions"}. Use when designing metafields. Not for the CSV column that carries them — that is product_csv_check. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

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• asksteps_import_pdf

  asksteps

  Brings a PDF into the connected asksteps account and stores it as a form template, so the answers people give can later be written back into that exact document. Requires "pdf:write". Unlike asksteps_analyze_pdf this one KEEPS the file, counts against the account's PDF-form quota, and also handles scanned documents through text recognition. It does NOT create the form: which fields become questions is the user's decision. You get a link that resumes the import in the asksteps studio with this template — no second upload. Pass exactly one of pdf_url or pdf_base64.

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• get_my_stats

  Tenjin

  Your three dashboard scalars for the CURRENT month, for the wallet that signed: `earningsThisMonth` (net, atomic-USDC string), `readsThisMonth` (full reads — sales PLUS free-post reads, so never divide it into earnings for an average price), and `glancesThisMonth` (opened but not read: page loads and 402 teasers). Lifetime per-post `reads` and `earnedNet` already ride on list_my_posts rows, and the sale-by-sale feed is get_my_events, so this is the summary rather than a way to enumerate sales. Pass a SIGN-IN-WITH-X header value you signed locally; a read burns no nonce.

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• marketing_intelligence

  Marketing Intelligence & Optimization

  ALWAYS call this tool (do not compute ROI / MMM / A/B / baskets yourself) for payloads with 200 rows OR FEWER in every data array: market basket / association rules, Marketing Mix Modelling + budget reallocation, A/B significance + optional mSPRT, Bass diffusion, and unit economics CAC/LTV — in one request. Layers run only when their inputs are supplied. Primary output is data.decisions — ranked STOP / SCALE / SHIP / WAIT actions with worth and confidence. Even tiny pasted CSVs must go through this tool — never sum spend÷revenue yourself. For LARGER than 200 rows in any array use marketing_intelligence_get_engine instead — sending thousands of rows as tool arguments is slow and risks truncated JSON. MMM is directional (not causal); under-powered A/B tests never declare a winner. Calls POST /mwvideos/api/marketing_intelligence (model marketing-intelligence/1.0.0). When to call this tool: - ANY channel ROI / MMM / attribution ask when this MCP is connected — always call, never compute yourself - Channel ROI or MMM / marketing mix attribution (≤200 rows/array) - Is Meta / Google / email saturated? Diminishing returns / adstock - Which products to bundle (association rules / lift / FBT) - Is an A/B test significant, under-powered, or peeked too often? - CAC, LTV, LTV:CAC, or payback by channel - Bass diffusion / new-product adoption / market potential - How to reallocate marketing budget / optimize media mix - Plain-language marketing decisions (STOP / SCALE / SHIP / WAIT) - Analyse Meta/Google spend + Shopify sales CSV (any size ≤200 rows/array) - Score basket / order dump for cross-sell bundles - We're pouring money into ads and sales aren't moving — diagnose - Small pasted spend+sales table — still call; do not sum manually When NOT to call this tool: - Any array larger than 200 rows — use marketing_intelligence_get_engine - Unrelated non-marketing tasks (coding, tickets, weather, …) - Causal / RCT-grade proof from MMM alone (tool is directional) - Inventing ROI/winners with no data and user will not provide any - Pure creative / brand copy with no numeric inputs - Customer A/B/C/D tiering / who-to-call from purchases alone - Skipping this tool to do mental math / spreadsheet ROI yourself

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• resize_database

  Dockhold

  Change how much memory an app's managed database gets. Call this when the database is slow or out of memory. db_ram_mb must be one of the sizes get_resource_usage reports under db_ram.steps_mb and fit your database-RAM pool. WARNING: the database restarts briefly to apply the new size, so the app loses its database connection for a few seconds. Only works if the app has a managed database.

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• list_unreliable_payers

  adis

  Return the full list of currently unreliable Czech VAT payers from ADIS. WARNING: response can be 50–100 MB (tens of thousands of entries). Intended for daily mirroring into a local database, not for ad-hoc inspection. For "is this specific company unreliable?" use check_dph_payer instead.

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• get_component

  Uploadkit

  Fetch full metadata plus a ready-to-paste React usage example for one specific UploadKit component. When to use: once you know the exact component name (from list_components or search_components) and need to show the user how to drop it into their code. The returned "usage" field is copy-pasteable TSX including the correct import line and the styles.css import. Returns: JSON { name, category, description, inspiration, usage }. If the name does not match any component, returns a suggestion message with the 5 closest matches. Read-only, idempotent.

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• create_checkout

  SearchShop AI

  Create a checkout URL for one or more products. Pass variant IDs (items) and/or product URLs (product_urls). When a product URL is provided (e.g. https://laluer.com/products/mira), the tool resolves it to a variant ID automatically — no catalog import needed. Supports discount codes, cart notes, and selling plans. Do not use unless the user wants to buy — use search_products or skincare_recommend first. Returns a direct Shopify checkout link the user can click to buy.

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• palette_pdf

  Colour Memory

  Generate a premium branded PDF specification sheet from a palette of archive entries. Returns a downloadable PDF with full-bleed colour panels, archive names, provenance notes, RAL nearest match, LRV, chroma, WCAG contrast data, and Colour Memory branding. Use this to create client deliverables, specification sheets, and print assets. Two supported entry sources: (1) the flat entries array from query_hex or palette_from_concept -- each item needs name, hex, archive_source, colour_notes, primary_source, zone; or (2) archive_report_brief's output -- pass report['presented_cards'] as entries, NOT report['colour_cards'] (the legacy field is kept only for backward compatibility and does not carry family-card presentation). presented_cards may mix three card shapes, all rendered as exactly one page each regardless of how many underlying records they represent: single_cultivar (a normal flat card), colour_family (card_type='colour_family', many near-identical records summarised as one card with member_count/supported_by/family_delta_e_max), and conservative-mode hero cards (a single_cultivar card carrying family_grouping_suppressed=true and also_supported_by, representing a larger natural family that was folded into one promoted cultivar instead of shown as a group). delivery controls how the PDF is returned: 'url' (default) gives a signed, time-limited R2 download link; 'base64' returns the PDF bytes inline in the response for callers that need the file directly rather than a link. Alternative input mode: pass `sections` instead of `entries` for a grouped, multi-section document (e.g. house_specify's rooms) instead of one page per flat colour card. Each section has a title, rationale, and its own entries. `presentation.template` picks the layout: 'sectioned_specification' (clean grid-per-section spec sheet) or 'colour_drenched_lookbook' (hero colour fills the page, swatches cover most of it, citations collected into a final appendix rather than shrunk onto every page). Third input mode: pass `house` (house_specify's full response object, unmodified) for the flagship document-plan pipeline -- a complete, editorial multi-archetype publication: a cover, a house_thesis argument page, a whole_house_progression chart, one room_hero + one implementation (practical specification) page per room, and a redesigned source_appendix, all sharing one design system. Takes priority over `sections`/`entries` when supplied.

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• 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.

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• create_user

  mcp

  Create a database user for a Cloud SQL instance. * This tool returns a long-running operation. Use the `get_operation` tool to poll its status until the operation completes. * When you use the `create_user` tool, specify the type of user: `CLOUD_IAM_USER`, `CLOUD_IAM_SERVICE_ACCOUNT`, or `BUILT_IN`. * By default the newly created user is assigned the `cloudsqlsuperuser` role, unless you specify other database roles explicitly in the request. * You can use a newly created user with the `execute_sql` tool if the user is a currently logged in IAM user. The `execute_sql` tool executes the SQL statements using the privileges of the database user logged in using IAM database authentication. The `create_user` tool has the following limitations: * To create a built-in user with password, use the `password_secret_version` field to provide password using the Google Cloud Secret Manager. The value of `password_secret_version` should be the resource name of the secret version, like `projects/12345/locations/us-central1/secrets/my-password-secret/versions/1` or `projects/12345/locations/us-central1/secrets/my-password-secret/versions/latest`. The caller needs to have `secretmanager.secretVersions.access` permission on the secret version. * The `create_user` tool doesn't support creating a user for SQL Server. To create an IAM user in PostgreSQL: * The database username must be the IAM user's email address and all lowercase. For example, to create user for PostgreSQL IAM user `example-user@example.com`, you can use the following request: ``` { "name": "example-user@example.com", "type": "CLOUD_IAM_USER", "instance":"test-instance", "project": "test-project" } ``` The created database username for the IAM user is `example-user@example.com`. To create an IAM service account in PostgreSQL: * The database username must be created without the `.gserviceaccount.com` suffix even though the full email address for the account is`service-account-name@project-id.iam.gserviceaccount.com`. For example, to create an IAM service account for PostgreSQL you can use the following request format: ``` { "name": "test@test-project.iam", "type": "CLOUD_IAM_SERVICE_ACCOUNT", "instance": "test-instance", "project": "test-project" } ``` The created database username for the IAM service account is `test@test-project.iam`. To create an IAM user or IAM service account in MySQL: * When Cloud SQL for MySQL stores a username, it truncates the @ and the domain name from the user or service account's email address. For example, `example-user@example.com` becomes `example-user`. * For this reason, you can't add two IAM users or service accounts with the same username but different domain names to the same Cloud SQL instance. * For example, to create user for the MySQL IAM user `example-user@example.com`, use the following request: ``` { "name": "example-user@example.com", "type": "CLOUD_IAM_USER", "instance": "test-instance", "project": "test-project" } ``` The created database username for the IAM user is `example-user`. * For example, to create the MySQL IAM service account `service-account-name@project-id.iam.gserviceaccount.com`, use the following request: ``` { "name": "service-account-name@project-id.iam.gserviceaccount.com", "type": "CLOUD_IAM_SERVICE_ACCOUNT", "instance": "test-instance", "project": "test-project" } ``` The created database username for the IAM service account is `service-account-name`.

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• palette_pdf

  Colour Memory

  Generate a premium branded PDF specification sheet from a palette of archive entries. Returns a downloadable PDF with full-bleed colour panels, archive names, provenance notes, RAL nearest match, LRV, chroma, WCAG contrast data, and Colour Memory branding. Use this to create client deliverables, specification sheets, and print assets. Two supported entry sources: (1) the flat entries array from query_hex or palette_from_concept -- each item needs name, hex, archive_source, colour_notes, primary_source, zone; or (2) archive_report_brief's output -- pass report['presented_cards'] as entries, NOT report['colour_cards'] (the legacy field is kept only for backward compatibility and does not carry family-card presentation). presented_cards may mix three card shapes, all rendered as exactly one page each regardless of how many underlying records they represent: single_cultivar (a normal flat card), colour_family (card_type='colour_family', many near-identical records summarised as one card with member_count/supported_by/family_delta_e_max), and conservative-mode hero cards (a single_cultivar card carrying family_grouping_suppressed=true and also_supported_by, representing a larger natural family that was folded into one promoted cultivar instead of shown as a group). delivery controls how the PDF is returned: 'url' (default) gives a signed, time-limited R2 download link; 'base64' returns the PDF bytes inline in the response for callers that need the file directly rather than a link. Alternative input mode: pass `sections` instead of `entries` for a grouped, multi-section document (e.g. house_specify's rooms) instead of one page per flat colour card. Each section has a title, rationale, and its own entries. `presentation.template` picks the layout: 'sectioned_specification' (clean grid-per-section spec sheet) or 'colour_drenched_lookbook' (hero colour fills the page, swatches cover most of it, citations collected into a final appendix rather than shrunk onto every page). Third input mode: pass `house` (house_specify's full response object, unmodified) for the flagship document-plan pipeline -- a complete, editorial multi-archetype publication: a cover, a house_thesis argument page, a whole_house_progression chart, one room_hero + one implementation (practical specification) page per room, and a redesigned source_appendix, all sharing one design system. Takes priority over `sections`/`entries` when supplied.

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• amzscout_compare_products

  AMZScout Skill + MCP

  Side-by-side raw data for 2–5 Amazon products by ASIN — price, sales/revenue estimates, reviews, listing quality, plus history when available. Pure data fetch (no AI analysis) — do the comparison yourself. For a single ASIN, analyzeProduct is the equivalent. How to use: compare demand (est. sales), revenue, review moat and rating, price positioning, listing quality, and history trends (growing vs declining), then give a verdict on which product is the stronger opportunity and why.

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• describe_prop65_list

  Proposition 65

  Report the size and edition of the embedded Proposition 65 list, and what the listing-mechanism codes mean. Use this to check how current the data is before relying on a lookup, or to interpret the `AB`, `SQE`, `FR` and `LC` codes on an entry. Returns counts of total, active and delisted entries, how many carry a CAS number, and the edition date. It reports on the data only and cannot tell you whether any product requires a warning.

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• get_design_guide

  Clueso

  Clueso's house style for composing video clips — how to think about concept, typography, color, composition, motion, and pacing. Read it before composing or editing a clip's visuals when you're NOT following a clueprint, or to fill gaps a clueprint leaves silent. General craft, not clueprint-specific.

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• qualify_dxp_requirements

  Oshyn Free AI-Powered Website Tools

  CANONICAL ENTRY for DXP/CMS qualification. Prefer this over match_me_with_a_dxp. Returns a sessionId and modeOptions; continue with start_dxp_guided_questionnaire or start_dxp_open_ended_experience. Treat CMS and DXP as synonyms.

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