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458,158 tools. Updated 2026-08-14 23:05

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

  • Get the company's brand guidelines — name, tagline, colors (hex codes), typography, personality/tone, naming rules, and VISUAL + POSITIONING dos/donts. Call this first, and use it, before generating any image, banner, video, or visual asset (inject the exact brand colors + style), and for naming/positioning in copy. For HOW to WRITE (voice, cadence, word choice) use get_voice_profile — this brand guide governs how the brand LOOKS and what it stands for, not writing style.
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  • 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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  • Create a brand voice profile to guide AI content generation. Configurable fields: • tone: Overall tone (professional, casual, energetic, witty, etc.) • personality: Brand personality description • writingStyle: Writing style guidelines • doRules: Array of things the brand voice SHOULD do • dontRules: Array of things the brand voice should NEVER do • examplePosts: Example posts in this brand voice • approvedHashtags: Preferred hashtags to prioritize • bannedWords: Words to never use in content • keyPhrases: Key phrases to incorporate naturally
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  • Generate a numbrU Compass profile for a person using the Compass engine. Returns personality insights, communication style, and relationship strategies.
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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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  • 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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Matching MCP Servers

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    A tiny local MCP server that learns a user's conversational style, catchphrases, dialect markers, emoji habits, tone preferences, and concrete collaboration preferences without storing private memories.
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    MIT

Matching MCP Connectors

  • Set the style guidance for an executive report

  • Writing Style Checker (WSC) is a prose linter with an AI-tells detector: alongside classic checks (weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, filler adverbs) it flags 190+ research-cited words, phrases, and structural patterns overrepresented in AI-generated text — each with an explanation and source.

  • Get Lenny Zeltser's Vuln one-page executive brief template. Standalone variant of `vuln_get_template` for callers that only want the brief without the long-form report. This server never requests your vulnerability notes and instructs your AI to keep them local—the brief template and guidelines 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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  • [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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  • Explain how to use a public prompt_style from the RetroDiffusion API. Use this before create_inference if you are unsure whether a style expects `input_image`, supports per-inference `reference_images`, or whether you meant style-level `style_reference_images`.
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  • The curated preset catalog for the no-AI style creation path, grouped by axis (art_style / narrative_style / director_style). Show the user the labels + descriptions and let THEM pick one per axis — don't choose silently. Art presets include preview image URLs (view_image works on them). Create with create_style(presets={axis: id, ...}) — instant, no analysis job. Full field text lands on the style row (get_style shows it after creation).
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  • Render one of a style's two template images — a REAL step of style setup, not an optional extra: a style isn't finished until both its character and environment templates are rendered (the app shows them on the style card). Asset reference images render against them (characters → character template; environments and objects → environment template), and segment renders fall back on them when a shot has no asset reference — so finish BOTH before generate_asset_reference. Run once per template_type ("character" | "environment") for every new style; skip types the style already has (get_style's `templates`). A template already exists is a hard stop here — the call refuses unless replace=True, because overwriting one silently re-anchors every future render. Async — await_jobs(style_id=...), then get_style.
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  • Generate a cohesive SET of custom images on a SOLID-COLOR background, each one a separate isolated subject sharing one background and one visual style (icons, logos, game assets, UI elements, sprite/asset packs). The style parameter says how everything is drawn; the subjects parameter says what to draw. The style can also come from reference images via the styleReferences parameter - alone, or best combined with the style text (text plus references holds a style tightest), so an existing set can be extended in its original style across many calls. Returns a zip download URL. Each call costs 1 credit. Run generation calls sequentially, never in parallel - only one generation runs at a time per API key. For TRANSPARENT-background output, use generate_transparent_image_set instead (3 credits per call). For a SINGLE composed picture or a full-bleed scene (hero image, banner, character portrait, environment), use generate_illustration instead. Output formats: PNG (lossless), JPEG, or WebP. Images can be delivered at a specific size or at their native resolution. Size and quality considerations: Omitting the size parameter delivers images at their native resolution with zero scaling, which produces the highest quality results and is recommended when images will be post-processed, composited, or resized downstream. Native output dimensions vary between generations and track the subject count - roughly 650-950px per side for small sets, down to roughly 400-650 at the full 18; fewer subjects means larger native images. Specifying a size (e.g. 512) guarantees consistent dimensions across all images and generations but applies resampling which may soften fine details. IMPORTANT - style and subject description rules for best results: The style applies to every image in the set, so it is what keeps them visually consistent. Put HOW the images are drawn (technique, palette, surface treatment) in the style, and make each subject description only about WHAT that one subject is, not how it looks. A quick test for any phrase: is it WHAT the subject is, or HOW it is drawn? HOW belongs in the style, shared across the set. - Get the style and the subject descriptions right with the user before you call. When their request puts how an image is drawn, a background, or a scene into a subject description (or names the subjects to draw in the style rather than as separate entries in the subjects list), fix it as you compose the call: routine moves of shared technique into the style you can just make, but when a change drops or alters something they explicitly asked for, tell them what you are adjusting and why first. Each call costs a credit, so it is worth getting this right up front rather than spending one on a framed or scene-filled result. - The style describes the visual treatment of the images (e.g. 'watercolor', 'pixel art', 'stained glass'). It must NOT mention background color, image count, layout, or sizing. - Do not list the subjects to draw in the style (e.g. 'illustrations of a fox, an owl, and a deer'); the style is only the shared visual treatment, and the subjects belong in the subjects list, one per entry. A category or theme word is fine (e.g. 'insect illustration'). - Do not put background color or background descriptions in the style or subject descriptions. - Avoid framing the style as a type of painted canvas ('oil painting', 'acrylic painting', 'gouache painting', 'pastel painting'). These tend to produce each image as a rectangular framed canvas with its own colored background, rather than an isolated subject. Prefer 'illustration' or a specific technique: 'watercolor illustration', 'pen-and-ink sketch', 'ink wash', 'relief-etching', 'pastel drawing', 'woodblock print'. - Avoid color-field or atmospheric phrasings in the style ('luminous backgrounds of violet, rose, and gold', 'set against jewel-tone fields', 'dreamlike rainbow atmosphere'). These instruct the image model to fill each image with colored atmosphere, producing framed compositions rather than isolated subjects. Describe only the linework, palette, and technique of the subjects themselves. - Do not describe an aged, weathered, cracked, or textured surface, ground, wall, panel, or paper that the whole artwork sits on ('on aged wood', 'cracked fresco wall', 'aged parchment surface'); name the art tradition(s) or style(s) instead ('fresco-style illustration'). Texture that belongs to a subject's own material is fine ('a weathered bronze shield', 'a cracked ceramic vase'). - No captions, labels, or annotations. Text that is part of the depicted object is fine (e.g. 'STOP' on a stop sign, 'EXIT' on an exit sign). - No grid lines, borders, frames, or separators. - No overlapping or collage-style arrangements. - Do not connect the subjects to each other or give them shared physical elements: no wires, cords, chains, ropes, ribbons, vines, or threads running between subjects, no frame or banner they share, no phrasing like 'connected by' or 'strung together', and no single continuous line or tube forming multiple subjects. Each subject must be drawable in complete isolation; connections inside one subject (a chain on an amulet, laces on a boot) are fine. - No dramatic/long drop shadows (subtle shadows are fine). - Image descriptions should describe WHAT to depict, not where to position it. - Each image is ONE isolated subject, not a scene. Describe the subject with its pose or action and anything it directly holds, rides, or interacts with, but not the surrounding setting, environment, landscape, or sky. For a single composed scene (a figure set within an environment), use generate_illustration instead. - Do not use size words (large, tiny, small, etc.) on the overall image subject (e.g. 'a large elephant', 'a tiny mouse') - all images are produced at the same size. Size words on details within the image are fine (e.g. 'a plate with a small insignia'). - Maximum 18 images per generation. Do not put the image count in the style. - Subjects must be distinct: entries that differ only in case, punctuation, or spacing count as the same subject and the call is rejected. Explicit filenames must be distinct too (a different extension alone is not distinct). - The style must actually describe a visual style, and each subject must name a drawable subject; text that does not is rejected. - Style description max length: 500 characters. Image description max length: 200 characters each. - Size: each axis between 256 and 512 pixels (e.g. "512" for square, "256x512" for non-square); values outside the range are rejected. Omitting size delivers native resolution, which is also the path to larger images. - If the style check returns a suggested cleanup, show the user the specific changes and get their confirmation, then resubmit the approved prompt with validation set to "skip" so it generates exactly as approved (resubmitting without "skip" re-runs the check and may return further suggestions). See the validation parameter for when to use "skip" and "auto-apply". - If a "Rate limit exceeded" error is returned, wait the suggested number of seconds before retrying. Do not retry immediately.
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  • Generate a cohesive SET of custom images with TRANSPARENT backgrounds, each one a separate isolated subject sharing one visual style (icons, logos, sprites, UI assets that need to drop onto any backdrop). The style parameter says how everything is drawn; the subjects parameter says what to draw. The style can also come from reference images via the styleReferences parameter - alone, or best combined with the style text (text plus references holds a style tightest), so an existing set can be extended in its original style across many calls. Returns a zip download URL. Each call costs 3 credits. If a solid colored background is acceptable for the user's use case, generate_image_set is 1 credit per call instead. Run generation calls sequentially, never in parallel - only one generation runs at a time per API key. Output formats: PNG (lossless) or WebP. JPG is not supported because it has no alpha channel. Size and quality considerations match generate_image_set: omitting size delivers native resolution (best quality, varies between generations); specifying a size resamples to that exact dimension. IMPORTANT - style and subject description rules for best results: The style applies to every image in the set, so it is what keeps them visually consistent. Put HOW the images are drawn (technique, palette, surface treatment) in the style, and make each subject description only about WHAT that one subject is, not how it looks. A quick test for any phrase: is it WHAT the subject is, or HOW it is drawn? HOW belongs in the style, shared across the set. - Get the style and the subject descriptions right with the user before you call. When their request puts how an image is drawn, a background, or a scene into a subject description (or names the subjects to draw in the style rather than as separate entries in the subjects list), fix it as you compose the call: routine moves of shared technique into the style you can just make, but when a change drops or alters something they explicitly asked for, tell them what you are adjusting and why first. Each call costs a credit, so it is worth getting this right up front rather than spending one on a framed or scene-filled result. - The style describes the visual treatment of the images (e.g. 'watercolor', 'pixel art', 'stained glass'). It must NOT mention background color, image count, layout, or sizing. - Do not list the subjects to draw in the style (e.g. 'illustrations of a fox, an owl, and a deer'); the style is only the shared visual treatment, and the subjects belong in the subjects list, one per entry. A category or theme word is fine (e.g. 'insect illustration'). - Do not put background color or background descriptions in the style or subject descriptions. - Avoid framing the style as a type of painted canvas ('oil painting', 'acrylic painting', 'gouache painting', 'pastel painting'). These tend to produce each image as a rectangular framed canvas with its own colored background, rather than an isolated subject. Prefer 'illustration' or a specific technique: 'watercolor illustration', 'pen-and-ink sketch', 'ink wash', 'relief-etching', 'pastel drawing', 'woodblock print'. - Avoid color-field or atmospheric phrasings in the style ('luminous backgrounds of violet, rose, and gold', 'set against jewel-tone fields', 'dreamlike rainbow atmosphere'). These instruct the image model to fill each image with colored atmosphere, producing framed compositions rather than isolated subjects. Describe only the linework, palette, and technique of the subjects themselves. - Do not describe an aged, weathered, cracked, or textured surface, ground, wall, panel, or paper that the whole artwork sits on ('on aged wood', 'cracked fresco wall', 'aged parchment surface'); name the art tradition(s) or style(s) instead ('fresco-style illustration'). Texture that belongs to a subject's own material is fine ('a weathered bronze shield', 'a cracked ceramic vase'). - No captions, labels, or annotations. Text that is part of the depicted object is fine (e.g. 'STOP' on a stop sign, 'EXIT' on an exit sign). - No grid lines, borders, frames, or separators. - No overlapping or collage-style arrangements. - Do not connect the subjects to each other or give them shared physical elements: no wires, cords, chains, ropes, ribbons, vines, or threads running between subjects, no frame or banner they share, no phrasing like 'connected by' or 'strung together', and no single continuous line or tube forming multiple subjects. Each subject must be drawable in complete isolation; connections inside one subject (a chain on an amulet, laces on a boot) are fine. - No dramatic/long drop shadows (subtle shadows are fine). - Image descriptions should describe WHAT to depict, not where to position it. - Each image is ONE isolated subject, not a scene. Describe the subject with its pose or action and anything it directly holds, rides, or interacts with, but not the surrounding setting, environment, landscape, or sky. For a single composed scene (a figure set within an environment), use generate_illustration instead. - Do not use size words (large, tiny, small, etc.) on the overall image subject (e.g. 'a large elephant', 'a tiny mouse') - all images are produced at the same size. Size words on details within the image are fine (e.g. 'a plate with a small insignia'). - Maximum 18 images per generation. Do not put the image count in the style. - Subjects must be distinct: entries that differ only in case, punctuation, or spacing count as the same subject and the call is rejected. Explicit filenames must be distinct too (a different extension alone is not distinct). - The style must actually describe a visual style, and each subject must name a drawable subject; text that does not is rejected. - Style description max length: 500 characters. Image description max length: 200 characters each. - Size: each axis between 256 and 512 pixels (e.g. "512" for square, "256x512" for non-square); values outside the range are rejected. Omitting size delivers native resolution, which is also the path to larger images. - If the style check returns a suggested cleanup, show the user the specific changes and get their confirmation, then resubmit the approved prompt with validation set to "skip" so it generates exactly as approved (resubmitting without "skip" re-runs the check and may return further suggestions). See the validation parameter for when to use "skip" and "auto-apply". - If a "Rate limit exceeded" error is returned, wait the suggested number of seconds before retrying. Do not retry immediately.
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  • Get a ready-made AI-generated icon pack (12 consistent icons) — e.g. a house style like 'clay-starter' / 'line-minimal-starter', or a pack generated in the MEASURED style of a decoded brand. Free and unmetered. Each image is a 1024px transparent PNG you can download and use directly (full commercial rights). Unknown slug → lists available packs.
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  • Use when the user wants you to BUILD or PROPOSE a brand-new portfolio for them — e.g. "build me a portfolio", "put together a dividend portfolio", "draft a portfolio of AI stocks", "create a new portfolio for $10k". Generates a REVIEWABLE paper-portfolio draft for the signed-in Bullrun user from a natural-language brief (e.g. "a diversified European dividend portfolio"). Requires OAuth with the write:drafts scope and a Bullrun Pro account. This is DRAFT-ONLY and never changes any live position: the draft is saved to the user's account and appears in the Bullrun Portfolio tab under "Pending AI drafts", where the user reviews it and explicitly accepts it to create a new portfolio (or discards it). To suggest additions to an EXISTING portfolio instead, use create_position_draft. Tickers are chosen only from Bullrun's priced stock/ETF universe; pass instrumentUniverse for stocks only, ETFs only, or a mix. If the brief is vague, first ask ONE quick round of up to three multiple-choice questions (investing style, region focus, and size), each with a default the user can accept with "just pick for me", then build; skip any dimension the user already specified and do not interrogate across multiple turns.
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  • Generate a single image from a text prompt through Frenchie. Required: prompt. Optional: style (free-text style direction), size, quality, format, background. stdio mode auto-saves the image to .frenchie/<slug>/generated.<ext>; HTTP mode returns a presigned imageUrl that the agent should download for the user.
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  • Get Lenny Zeltser's expert writing guidelines for security reports and assessments. Provides guidance on tone, structure, clarity, executive summaries, and avoiding common writing mistakes. Includes rating-sheet items (the four lens sheets: structure, look, words, tone) as concrete reference points for grounded feedback. Works for any security document. This server never requests your documents and instructs your AI to keep them local—guidelines flow to your AI for local analysis. Note: For incident response reports specifically, use the ir_* tools which provide deeper section-by-section review criteria.
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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 expert security assessment report writing guidelines. Topics: severity (the risk-adjusted severity model — the spine), findings, remediation, methodology, scope, strengths, brief (one-page brief section guidance), executive_summary, analysis, anti_patterns, frameworks, handoffs, and summary. The general 'tone' topic defers to `get_security_writing_guidelines` for the canonical Five Elements rules. 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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