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205,128 tools. Last updated 2026-06-15 09:49

"Enforcing Apple Brand Style Guidelines for Team Communication Drafts Without AI Style Learning" matching MCP tools:

  • Read-only. Use when the user wants to inspect saved project brand styles for generated email drafts. Returns paginated project brand style summaries, ids, default status, style metadata, and app URLs. Do not use to create, update, delete, or expose raw style provenance.
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  • Fuzzy-search the UploadKit component catalog by any free-text keyword — component name, category, description, or design inspiration (e.g. "apple", "stripe", "vercel", "terminal", "progress ring", "kanban board", "matrix"). When to use: the user describes the vibe or use case but does not know the component name yet ("I want something like Stripe Checkout", "show me Apple-style uploaders"). Prefer this over list_components when the goal is discovery rather than enumeration. Returns: JSON { query, count, matches: [{ name, category, description, inspiration }] }. Read-only, idempotent, case-insensitive.
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  • Get Lenny Zeltser's CTI one-page executive brief template. Standalone variant of `cti_get_template` for callers that only want the brief without the long-form report. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.
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  • Get Lenny Zeltser's IR one-page executive brief template. Standalone variant of `ir_get_template` for callers that only want the brief without the long-form report. This server never requests your incident notes and instructs your AI to keep them local—guidelines flow to your AI for local analysis.
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  • [FIND] START HERE when you know what you want. Free-text search across every active RRG listing. This endpoint answers DEFINED intent, not open browse. Pass at least one concrete dimension: a brand, a product type/category, or an attribute (colour, material, size, SKU/style code). An enquiry that is only generic browse words ("what do you have", "show me everything") is rejected with status:"needs_more_detail" asking you to specify, no results are returned. To browse without intent, call list_drops instead. Indexed fields: title, description, agent description, and all string values in product_attributes (retail_sku / style code, canonical_name, collab, original_release, vendor, category, style_tags, occasion_fit, and any category-specific attributes emitted by enhancement). Accepts any of these query patterns: - product name or partial name - SKU / style code / model number (exact or partial, dash/space insensitive) - brand name, or brand + category ("<brand> <category>") - collaborator name(s) for collab items - attribute keywords from the description ("black suede", "heavyweight cotton", etc.) Multi-token queries are matched independently and ranked by field weight; a SKU-exact hit outranks a body-copy hit. Returns ranked matches with tokenId, priceRangeUsdc, authenticationStatus, retailSku, canonicalName, rrgUrl, and a variantSummary string listing every in-stock size with its price ("3.5=$1583, 4=$1899, 10.5=$770, …"). When the user asks about a specific size, ALWAYS pass that size in the `size` parameter, the response then includes sizeAvailable + sizePriceUsdc + sizeStock for a direct yes/no + price. For queries like "size 10.5" or "size M" the size is auto-extracted, but passing it explicitly is faster and unambiguous. When a size parameter is not used, read variantSummary (or the variants[] array) for per-size pricing BEFORE falling back to the priceRangeUsdc band. Per-size prices are exact; the band is only a floor→ceiling range. Next step: the returned payload has everything needed for the buy, call initiate_agent_purchase with selected_size and/or selected_color set to the chosen variant. Pass selected_color whenever the listing has a colour axis (variants[].color non-null) so fulfillment ships the right finish. get_drop_details is optional (adds signed image URLs + shipping context). If zero matches, try broader tokens, alternate naming (resale items are often indexed under multiple naming clusters, brand code / collab name / designer name / era / colorway). If still zero, call list_drops to browse.
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  • Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.
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Matching MCP Servers

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    Provides a catalog of curated author writing styles and tools to blend or analyze them across eight dimensions for text and image prompt generation. It enables users to apply structured literary patterns through deterministic style modeling and coordinate-based interpolation.
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  • Search Apple Maps businesses with Apple ratings and aggregated Yelp + TripAdvisor reviews.

  • A design-style library for AI agents: search real styles, fetch a ready-to-apply design spec.

  • Create a hosted link-in-bio page draft from a style preset. Provide title, displayName, and a preset (or 'auto' to infer from businessType/style). The page starts with empty placeholder blocks for you to fill in via block.update — do not invent content. This tool intentionally creates a draft only and does not return a publish action. After calling it, stop and show the preview URL. Do not call page.publish in the same turn unless the user's current message explicitly asks to publish, make the page live, or get a public share link. Anonymous demo pages expire unless claimed.
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  • Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.
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  • Retrieve pre-synthesized per-session memory dossiers (typed: experience | fact | preference; with When/Involving/To-purpose metadata). Use for multi-session or preference-style questions where stitching across conversations is the bottleneck — the dossier already summarises each session's key events. Two modes: mode='search' with a query (BM25-ish ranking over summary+purpose, optional type_filter), or mode='list' returns the tenant's most-recent dossiers chronologically. Tenants without FEATURE_SESSION_DOSSIERS enabled return an empty list (no error).
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  • Extracts and parses JSON from mixed-content text. Handles LLM output with JSON embedded in prose, code fences (```json), trailing commas, single-quoted strings, JS-style comments, and bare object keys (JSON5-style). Returns the parsed data, a cleaned JSON string, extraction method used, and any repair applied. Pure text processing — zero external API calls.
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  • Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.
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  • Get Lenny Zeltser's Security Assessment cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `assessment_load_context`. This server never requests your assessment notes or report and instructs your AI to keep them local—the templates and guidelines flow to your AI for local analysis.
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  • Get a list of all available themes with style descriptions and recommendations. Call this to decide which theme to use. Returns a guide organized by style (dark, academic, modern, playful, etc.) with "best for" recommendations. After picking a theme, call get_theme with the theme name to read its full documentation (layouts, components, examples) before rendering. This tool does NOT display anything to the user — it is for your own reference when choosing a theme.
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  • Get Lenny Zeltser's Malware frameworks (primary frameworks the brief structurally derives from) plus optional sibling frames (adjacent frameworks that aren't the structural backbone). Pass `include_siblings: false` to skip sibling blocks. This server never requests your sample, analysis notes, or indicators and instructs your AI to keep them local—guidelines and the report template flow to your AI for local analysis.
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  • Generate archive-grounded colour names for up to 40 product SKUs. Input: list of hex values, product category, brand name, naming style. Output: for each hex -- archive name, source citation, one-line product description, dE2000 match distance, match quality, and confidence score. Every name is archive-sourced, not invented. Each carries a primary source citation that can be defended to buyers, press, and brand teams. Use for paint ranges, candle collections, fashion lines, homeware, cosmetics. Style options: geographical, poetic, material, literary, mixed.
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  • Build a complete creative intelligence profile from internal brand documents — creative briefs, brand guidelines, product specs, customer research, competitive analysis. Takes any mix of file_ids (from a previous upload), document_urls (public PDF/DOCX/TXT/MD links, up to 10), or documents_inline (base64-encoded files with filename), plus an optional context_url for layering live brand context (colors, fonts, current messaging) and optional idempotency_key. Returns a job_id; poll with get_powersource. Output shape is identical to create_powersource_url: identity, offer, selling points, voice, buyer profile, tensions, angles, emotional arcs, ctas, narrative. Use this when the user says "I have a brief", "here's my brand guidelines", "use this document", drops a PDF / DOCX / strategy deck, or when the truth lives in internal materials rather than the public website. The pipeline reads text only — convert PDFs to markdown before submitting via documents_inline when possible. Costs 100 credits. Do NOT use for URL-only scans — use create_powersource_url. For URL + docs combined (highest fidelity, triangulates public messaging against internal strategy), use create_powersource_full.
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  • Get Lenny Zeltser's Malware cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `malware_load_context`. This server never requests your sample, analysis notes, or indicators and instructs your AI to keep them local—guidelines and the report template flow to your AI for local analysis.
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  • Get Lenny Zeltser's Security Assessment one-page executive brief template. Standalone variant of `assessment_get_template` for callers that only want the brief without the long-form report. This server never requests your assessment notes or report and instructs your AI to keep them local—the templates and guidelines flow to your AI for local analysis.
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  • Get Lenny Zeltser's CTI frameworks (primary frameworks the brief structurally derives from) plus optional sibling frames (adjacent frameworks that aren't the structural backbone). Pass `include_siblings: false` to skip sibling blocks. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.
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  • [Read] Reddit/Discord/Telegram/YouTube-style UGC: non-empty query uses vector API; coin without query uses OpenSearch. Both empty invalid. X/Twitter narrative -> search_x; headlines -> search_news. Not macro economic statistics; not structured event list -> get_latest_events.
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