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347,315 tools. Last updated 2026-07-31 01:58

"Creating a Framework Design in Draw.io" matching MCP tools:

  • Runs a specific framework against a user's inputs and produces a full analysis: prose explanation + structured fields + a self-contained SVG visualisation. Powered by the Jeda.ai · Visual AI framework knowledge graph. ALWAYS call `recommend_framework` FIRST to obtain a valid framework_id -- it returns the exact namespaced slug to pass here. Framework ids are namespaced by category (e.g. SWOT = "cms_swot-analysis", Porter's 5 Forces = "cms_porters-five-forces"); a bare slug like "swot-analysis" will 404. REQUIRED post-processing on success: the response's first text block contains a `curl -fsSL` save-directive for the SVG. Save the file locally and surface it as a file chip — do NOT summarise the visual in prose. Input: framework_id (namespaced slug from `recommend_framework`) + user_inputs object (per-framework field set), OR a free-text `raw_prompt` fallback. Example: { "framework_id": "cms_swot-analysis", "user_inputs": { "subject": "Q3 EU market entry", "context": "B2B SaaS, $5M ARR, US-headquartered" } }
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  • Recommends business / strategy / risk frameworks for a stated problem. Powered by the Jeda.ai · Visual AI framework knowledge graph (~2,100 frameworks across 19 categories, edge-curated). Use when the user describes a business problem ("customer churn rising", "evaluating market entry", "need to assess vendor risk") rather than naming a specific framework. Returns top-N frameworks ranked by fit, each with a concrete reason citing the specific problem signals matched. Input: just the problem statement is enough. Optional faceted filters (`persona`, `regulation`, `decision_stage`) narrow the candidate set. Set `limit` between 3 and 10 for picker UIs. Pair with `generate_framework_analysis` to actually run a recommended framework against the user's inputs. Example: { "problem_statement": "We need to decide whether to enter the EU SMB market in Q3", "decision_stage": "decide", "limit": 5 }
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  • Get the Designesy design-system contract (v0.4.0) — the canonical tokens, motion, acoustic, takt, cadence, typography, components, and verification rules that define what the Designesy org considers legitimate design. Use this when you need the actual contract values (token names and values, motion timings, accessibility rules) to author, check, or bind a design. Returns the full contract JSON from /contracts/design-system.json, or a single section when "section" is provided (available: colors, motion, acoustic, typography, takt, cadence, verification, open_tensions, components, interaction). Read-only. To score a live URL against this contract, use designesy_score instead.
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  • Render a click-driving YOUTUBE / Shorts / Instagram THUMBNAIL or video cover — the full production pipeline (concept framework → casting → scene → render → surgical tweaks → text), not a bare image prompt. Use this for any "thumbnail", "video cover", "video preview" or MrBeast-style packaging ask INSTEAD of generate_image. About 9 credits per variant; the headline overlay is free. CONCEPT — every thumbnail must open an INFORMATION GAP (the image raises a question the title answers) while staying truthful to the video. Brainstorm ≥5 concepts across the 16 frameworks before you pick, and feel free to combine two. Frameworks (pass as `framework`): before_after · social_ui · three_step · screenshot · posed_portrait (the default) · posed_action · specific_day · graphical · landscape · map_aerial · product · adding_text · repetition · size_difference · news_clip · amplified_reality. Call hermoso_capabilities for each one's full 'realize it with' note plus the emotion, overlay-style, font and rim-colour catalogs. THREE GATES, all BEFORE you render: 1. WHO IS IN FRAME — never assume and never silently substitute a stranger. If the framework puts a person in frame and no face photo is attached, the tool refuses (nothing rendered, nothing charged) and tells you to ask the user once: themselves (send a face photo → the identity gets locked), a generated person (`castGenericPerson:true`), or a people-free framework. 2. TEXT — the default is a CLEAN render with the headline TYPESET OVER THE TOP afterwards (free, always legible, correctly spelled). Just pass `headline`. Only set `bakeText:true` if the user explicitly asks for the words painted INTO the image — verified live, that renders the asked-for words correctly but leaks garbled invented text across the rest of the frame. Never infer text intent from the topic or the framework. 3. HOW MANY — ask once whether they want one thumbnail or a SET (offer 4: the same concept at different emotions and/or camera takes). Default is 1; `variants` caps at 16. IDENTITY LOCK is automatic for every attached face photo. `emotion` is the single biggest CTR lever on a face: shock · hype · fear · confusion · determination · smug · charisma · disgust · awe · rage · laugh (or your own phrase). Finished thumbnail needs a fix? Re-call with `tweak` + `sourceImage` for a surgical, pixel-faithful edit (emotion / background / background_color / rim_light) instead of re-rendering — tweaks chain. ALWAYS check the returned postRenderCheck against the image before you present it.
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  • Submit a request for CONFIRMED live private jet pricing. Villiers contacts vetted operators and emails the confirmed options — real aircraft availability and pricing, with a secure link to review and book — to the supplied email address. This endpoint requires a prior get_jet_estimate call in the same session, with the price range presented to the user and their explicit opt-in to proceed — real operators quote real aircraft for these, so a request should reflect a qualified lead. Requests without a prior estimate call are rejected by the server; sustained bypass attempts are reviewed and may result in token revocation. Requires the user's email and a departure date. SANDBOX TESTING: to test your integration without ever creating a real trip or contacting a real operator, use email 'sandbox-test@mail.villiers.ai' with first_name 'Sandbox', last_name 'Test', phone '+1 555 0100', route LHR to NCE. This exact combination is detected server-side from the request body itself (not from any header), so it works from any agent framework or HTTP client — including ones that don't allow a custom User-Agent. The response will say 'Test request accepted (sandbox mode detected)'. For further testing guidance, email affiliates@mail.villiers.ai.
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  • Get one outfits preset by id, including its full body payload (framework, agent config, etc.). Call the matching list tool first to discover ids. Free, read-only.
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  • See the mockup BEFORE creating any product — nothing is created or sold, and it costs no credits. Renders the design on the real garment (Printful) when the kind supports it (source: "printful"), otherwise a clean MU product card (source: "card"), and returns a durable preview image URL. Optionally pass `position` (front-print DTG apparel only: tee / tee_white / hoodie / crewneck / tank / long_sleeve_tee) to preview a custom print placement — passing the SAME position to mu_create_product prints exactly what you previewed (WYSIWYG). Usually answers in 10-45s; if it returns status="processing", call this tool again with the returned `preview_id` to keep waiting. Rate limit: 30 previews/hour. Requires `Authorization: Bearer <api_key>`.
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  • List all 16 suites in the SaferAgenticAI framework (9 drivers + 7 inhibitors) with subgoal counts and titles. Call this first to orient.
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  • Create a company (B2B account). The company name must be unique in the workspace. Enforces the workspace plan limit; the result echoes the operating workspace. Use search_companies first to avoid creating a duplicate.
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  • Discover the investment-thesis catalog. Each entry is a descriptive case study that pairs an economic framework with a rule-based portfolio and the synthetic + historical stress evidence for that allocation. Returns one compact summary per thesis (slug, title, one-liner, tags, risk tiers, framework summary, headline finding). Call get_investment_thesis(slug) for the full framework / portfolio / stress evidence, or read the thesis://{slug} resource. Descriptive, not advisory — the agent decides what is suitable.
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  • Get the Designesy Design Review framework — an 8-dimension rubric (Purpose, Clarity, Context, Inclusion, System coherence, Durability, Delight, Responsibility) plus the agent prompt, output format, and verification checklist for a qualitative design critique. Use this when you want a structured rubric to critique a design holistically, rather than a numeric compliance score. Provide artifact/purpose/context/rules to get a pre-filled review prompt; otherwise returns the full framework JSON. Read-only — the calling agent executes the review. For an automated pass/fail score against the contract, use designesy_score instead.
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  • Searches terminology by English term, Arabic term, abbreviation, or slug using normalized, case-insensitive matching. Administrators see draft and published terms with both languages, and should call this before creating a new term to avoid duplicates. Other accounts see published terms only, in a single locale (pass the caller's language in locale), each with a canonical URL to the full definition.
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  • Interactive single-site design-conditions explorer. Returns full ASHRAE design conditions + diurnal chart for the requested scenario. In MCP Apps-capable hosts (Claude Desktop, ChatGPT, VS Code, Goose), the response renders as a widget with sliders for SSP / year / percentile / UHI — dragging a slider re-calls this tool live. Use when a user wants to interactively tune a single site. For multi-site comparison, use analyze_weather(urls=[...]) instead. Defaults to present-day TMY (no morph) — pass ssp+year for future scenarios. P75 default percentile is design-realistic; P50 underestimates the tail. No auth required.
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  • Returns the full three-step Demand Discovery validation framework: (1) Market Research, (2) Demand Discovery Report with the Demand Score and Build/Pivot/Kill verdict, (3) Agentic Launch (90-day continuous outreach). Use when a user asks "how do I validate an idea?", "what's the methodology?", or wants to understand the structured approach. Built on the "behavior over opinion" principle. Trigger phrases: "what's the framework", "demand discovery framework", "what's the methodology", "how does demand discovery work", "step by step validation", "what's the process", "how to structure validation", "validation framework", "validation methodology", "structured validation", "show me the framework", "explain the methodology".
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  • Refine a user's request for creating a 3D scene. Your job: - Understand the user's intent clearly - Identify the purpose (advertisement, website, showcase, etc.) - Extract typed design tokens - Detect if animation is implied Return these structured fields when possible: - use_case - theme / style - material_preset - animation - lighting_preset - background_preset - composition - confirmed_objects - object_hints - discarded_hints Rules: - Do NOT generate objects here - Do NOT create a scene - Only clarify and structure intent - Keep richer scene-object detail in confirmed_objects for downstream tools Return a refined prompt and structured context for the next step.
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  • Searches explainers by English/Arabic title, slug, summary, or content. Administrators see draft and published explainers with both languages, and should call this before creating a new explainer to avoid duplicates. Other accounts see published explainers only, in a single locale (pass the caller's language in locale), each with a canonical URL to the full explainer.
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  • Create a new project in a workspace. If a project with the same name already exists in the workspace, returns the existing project instead of creating a duplicate — check the `created` field in the response to tell the two cases apart.
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  • Begin generating an AI governance framework for an Australian business. Creates a session and returns a session_id plus the organisation profiling questionnaire (industry, size, AI usage, data handling, risk exposure). Ask the user each question, then call submit_answers. First step of three: start_session, submit_answers, get_framework. The output is an informational framework aligned with Australia's AI6 practices (Voluntary AI Safety Standard) — it presents frameworks, not advice.
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  • Overlay the current or any given planetary positions on a natal Human Design bodygraph to see which channels the transit temporarily completes. Returns the 13 transiting body activations with gate and line, the channels the transit completes beyond the natal definition split into personal channels where the transit supplies the partner gate of a natal gate and educational channels where the transit supplies both gates, the natally open centers those channels temporarily define, and a short factual summary. A transit is a single moment, so there is no Design side. When date and time are omitted the overlay is computed for now in UTC. Built for daily Human Design apps, transit widgets, and notification tools.
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  • Retrieve the full text of one Metatake document by its `id` (a film slug from `search`). Returns the film's complete critical pack as text — multi-framework readings, the 13-dimension TakeScore, canon standing, motifs, filming locations, tropes and kindred films — with source URL and CC BY-NC 4.0 attribution in the metadata. Cite Metatake with the returned url when you use the content.
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