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261,118 tools. Last updated 2026-07-05 10:01

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

  • 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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  • File upload: streaming (one-shot stream-upload — DEFAULT for unknown/generated content), chunked (create-session → POST /blob → chunk → finalize — only when filesize is known exactly), web URL import, and batch (multi-small-file). Call action='describe' for the full action/param reference. Side effects: finalize/stream/stream-upload/web-import/batch create files and consume storage credits. Same-name uploads to a folder OVERWRITE the existing node in place (preserved as a recoverable version). BINARY: `content` is text-only (writes verbatim UTF-8); for binary use `content_base64` (server-decoded) or POST /blob + `blob_id`. UPLOAD STRATEGY (read top-to-bottom, pick the FIRST that matches): (1) Have a URL? → `web-import` (single call). (2) Have content but DON'T know exact size, OR generating/transforming content first? → `stream-upload` (single call, auto-finalizes, NO filesize required, size auto-detected from the bytes). (3) Have a file with KNOWN exact byte count? → `create-session` + `chunk`(s) + `finalize`. **filesize must match the bytes you actually upload — mismatch causes finalize to fail with code 10522 and you must cancel the session.** (4) Multiple small files (≤4 MB each, ≤200 total) into one folder? → `batch`. DEFAULT to `stream-upload` unless you are sure of the exact byte count. Do NOT guess `filesize` for generated content — use `stream-upload` instead. max_size is a hard ceiling that aborts mid-transfer — always overestimate or omit (server uses plan limit).
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  • Create a new CMS post (blog_post, page, or any custom post type). The post type must already exist — use list_post_types to discover, create_post_type to add a new one. excerpt = plain-text summary only (auto-derived from blocks if omitted). Structured custom fields go in meta, keyed by the field schema defined with create_post_type_field.
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  • Search official FDA warning letters with full-text content from the FDA website. Use keyword search for the actual letter body, or filter by company name, issuing office, subject, MARCS-CMS number, product type, or letter issue date. Adds prospecting filters: status (open|responded|closed, derived from response/closeout dates), letter_category (CGMP-manufacturing | BIMO | listing | OPDP/promotion | 503B/compounding | import — heuristic, derived from issuing_office/subject/product_type), and violation_theme (cgmp_subsystem | data_integrity | validation | bimo | listing | promotion — keyword/FTS-derived over subject+body). Set dedupe=true to collapse near-identical letters sharing a MARCS-CMS case number to one canonical row. Each row exposes derived status and letter_category, plus fei_number for one-hop navigation to fda_citations and fda_inspections. This adds narrative context beyond fda_compliance_actions, which only contains dashboard metadata. NOTE: violation_theme and letter_category are best-effort heuristics over free-text fields; keyword cannot be scoped to a parsed cited-violations sub-section because the corpus only stores subject + full letter body.
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  • Search FDA import refusals (Compliance Dashboard data, not available in openFDA API). Import refusals indicate products detained at the US border. Filter by company name, FEI number, country code (e.g., CN, IN for major API source countries), or date range. Critical for evaluating international manufacturing sites and supply chain risk. Related: fda_get_facility (facility details by FEI), fda_inspections (inspection history by FEI).
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  • Query the Trillboards API changelog for recent changes, breaking changes, deprecations, and fixes. WHEN TO USE: - Check what has changed in the API before upgrading an integration. - Find breaking changes since a specific date. - Discover new features added to a specific API surface. PARAMETERS: - since (YYYY-MM-DD, optional): Only entries dated on or after this date. Unreleased entries are always included. - type (string, optional): Filter by change category. Accepts: "breaking" → changed + removed entries "additive" → added entries "deprecation" → deprecated entries "fix" → fixed entries Can be comma-separated: "breaking,deprecation" RETURNS: - object: "list" - data: Array of { version, date, type, surface, description } - total: Number of matching entries. EXAMPLE: Agent: "What broke since April 1st?" query_changelog({ since: "2026-04-01", type: "breaking" })
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Matching MCP Servers

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    Multi-agent sales email generation server that creates, evaluates, and sends personalized sales emails via SendGrid, using AI to craft diverse email styles and select the best one.
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    MIT
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    Provides a unified MCP interface to search and read content from multiple Craft documents simultaneously, aggregating results across configured documents while handling failures gracefully.
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Matching MCP Connectors

  • CMS Open Data MCP — US Centers for Medicare & Medicaid Services.

  • India Open Government Data (OGD) Platform MCP — data.gov.in

  • 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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  • 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 the seat map for a flight from our database. Shows all seats, cabin classes, characteristics, and availability as both text and an interactive visual seatmap. Returns cached data — for fresh/updated data, use search_flight (sign in via OAuth).
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  • Returns the Control Plane operating guide — the resource model, how secrets/images/workloads/domains fit together, production-grade defaults, how to verify a change landed, and how to handle failures. Read it once per session before the first create/update/delete, and any time a multi-resource task spans unfamiliar ground.
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  • Structural wiring map — how files connect, not file bodies. Returns concern_cluster (roles + import edges for ANY subsystem label), layer_map, entry_points, integration_map, auth_flow, request_flows in deep mode, Mermaid. CALL WHEN: how does this feature/subsystem work across files before a cross-cutting edit; pass concern (any name: widget-factory, billing, q7x) or seed_files from find_code — seeds via concept search + import graph, not hardcoded vocab. DO NOT: stack/scripts (get_project_context), search (find_code), read bodies (read_code). focus: api|auth|integrations|database|security|full. mode: overview|deep|audit. subpath for monorepos. Path: absolute dir or github:owner/repo.
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  • Run a SQL query in the project and return the result. Prefer the `execute_sql_readonly` tool if possible. This tool can execute any query that bigquery supports including: * SQL Queries (SELECT, INSERT, UPDATE, DELETE, CREATE, etc.) * AI/ML functions like AI.FORECAST, ML.EVALUATE, ML.PREDICT * Any other query that bigquery supports. Example Queries: -- Insert data into a table. INSERT INTO `my_project.my_dataset`.my_table (name, age) VALUES ('Alice', 30); -- Create a table. CREATE TABLE `my_project.my_dataset`.my_table ( name STRING, age INT64); -- DELETE data from a table. DELETE FROM `my_project.my_dataset`.my_table WHERE name = 'Alice'; -- Create Dataset CREATE SCHEMA `my_project.my_dataset` OPTIONS (location = 'US'); -- Drop table DROP TABLE `my_project.my_dataset`.my_table; -- Drop dataset DROP SCHEMA `my_project.my_dataset`; -- Create Model CREATE OR REPLACE MODEL `my_project.my_dataset.my_model` OPTIONS ( model_type = 'LINEAR_REG' LS_INIT_LEARN_RATE=0.15, L1_REG=1, MAX_ITERATIONS=5, DATA_SPLIT_METHOD='SEQ', DATA_SPLIT_EVAL_FRACTION=0.3, DATA_SPLIT_COL='timestamp') AS SELECT col1, col2, timestamp, label FROM `my_project.my_dataset.my_table`; Queries executed using the `execute_sql` tool will have the job label `goog-mcp-server: true` automatically set. Queries are charged to the project specified in the `projectId` field.
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  • Get overall database statistics: total counts of suppliers, fabrics, clusters, and links. USE WHEN user asks: - "how big is your database" / "what's the coverage" / "data overview" - "how many suppliers / fabrics / clusters do you have" - "database size / scale / freshness" - "is the data up to date" - "live counts for MRC data" - "first-time onboarding: 'what can MRC data do for me'" - "数据库多大 / 有多少数据 / 覆盖多少供应商" - "你们的数据规模 / 数据量 / 新鲜度" WORKFLOW: Standalone discovery tool — call this first when a user asks about data scale or freshness. Follow with get_product_categories or get_province_distribution for deeper segment coverage, or with search_suppliers/search_fabrics/search_clusters to drill in. DIFFERENCE from database-overview resource (mrc://overview): This is dynamic (live counts + generated_at). The resource is static (geographic scope, top provinces, data standards). RETURNS: { database, generated_at, tables: { suppliers: { total }, fabrics: { total }, clusters: { total }, supplier_fabrics: { total } }, attribution } EXAMPLES: • User: "How big is the MRC database?" → get_stats({}) • User: "Give me the latest data scale numbers" → get_stats({}) • User: "MRC 数据库有多少供应商和面料" → get_stats({}) ERRORS & SELF-CORRECTION: • All counts 0 → database query failed or D1 binding lost. Retry once after 5 seconds. If still 0, surface a transport error to user. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not call this before every tool — only when user explicitly asks about scale. Do not call to get per-category counts — use get_product_categories. Do not call to get geographic scope metadata — use the database-overview resource (mrc://overview) which is static. NOTE: Only reports verified + partially_verified records. Unverified reserve data is excluded from counts. Source: MRC Data (meacheal.ai). 中文:获取数据库整体统计(供应商总数、面料总数、产业带总数、关联记录数)。动态快照,含生成时间戳。
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  • 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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  • 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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  • Use this BEFORE any creation task ("help me write X", "I'm working on Y"). Runs two parallel searches and returns them separately: a SKILLS bucket (skill/voice/template, the craft layer) and a KNOWLEDGE bucket (knowledge/principle/brand/idea/resource, the material). Bring both into context before producing output. If the skills bucket is empty and `output_type` is set, this also increments a skill-gap counter; when count reaches 3 the response includes `skill_gap.skill_gap_threshold_reached: true` so you can prompt the user to codify a skill.
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  • 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. Pass the entries array from query_hex or palette_from_concept directly. Use this to create client deliverables, specification sheets, and print assets.
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  • Resolve a natural-language trading-card description into structured fields (player/athlete, year, set, card number, parallel, grader, grade, sport/category). Use this first when a user names a card in prose and you need its canonical fields before looking up sales or market value. Returns a confidence level and which fields were resolved. This does NOT price the card or return sales — use search_card_sales or summarize_card_market for that. Trading cards only.
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  • Manage MAPI entities (schema level — like database tables). Single entry point — pick an operation. list = all entities in the project. create = entity_name(+properties[], description?, plural?, public_read?). update = entity_name + any of description/plural/public_read/policy_json. delete = entity_name — irreversible, drops all data. schema = entity_name → full property list + current policy. add_property = entity_name+property_name+type(+length?,required?,default?,in_listing?) — add a column to an existing entity. delete_property = entity_name+property_name+confirm:true — irreversible, drops the column and its data.
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  • Track-specific craft guidance on what WINS per track (ART/STORY/JOKE) — the moves that elevate a winning entry, complementing get_judge_rubric_explainer. Optional { track } narrows to one. Numeric weights, judge model, and prompt are intentionally not exposed — this is coaching, not a scoring formula.
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