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217,360 tools. Last updated 2026-06-20 15:31

"Generating a series of images using MCP from a sample image" matching MCP tools:

  • Display the user's images inline — one or many. Users speak plainly and will NOT know asset ids; never ask for one, resolve it yourself. For "show me" or "show me my last image" call with NO arguments (shows the most recent image). For "show me my last 4 images / my last 10 pictures" pass count=N (returns a clean grid, up to 12). For a specific known image pass assetId. Renders a branded SwitchApp media card with a Download action per result; do not just print URLs. (Videos are not shown here — use list_my_videos and return the newest finished video's view_url, which plays.)
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  • Generate an AI image using Avocado AI. Returns a jobId immediately; image generation completes in 10-60 seconds. After calling, use the check_job tool with the returned jobId to retrieve the result, once complete, check_job returns the image inline so it renders directly in chat. Run models_list to see available models. Costs 1-4 credits per image depending on model and quality.
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  • Create a new Avocado AI Flow pre-built with a node-graph pipeline, and return its id and direct URL so the user can open it on the canvas. You design the whole pipeline: pass the nodes and edges and the server validates socket compatibility, aligns video models to the input shape, lays the graph out left-to-right, and adds a caption per step. Edges reference nodes by 0-based index in the `nodes` array. This creates (does not run) the flow — the user runs it from the editor. Use the capability map below to choose node types, models, and handles: You are Avo, a senior creative-workflow designer inside Avocado AI's Flow editor. The user describes a creative goal; you respond with a node-graph proposal that the editor previews on the canvas. Think like a production director: design the FULL pipeline needed to get a polished result, not the minimum number of nodes. DESIGN PRINCIPLES — build capable, complete pipelines: - Match the pipeline's ambition to the request. A throwaway test is 2-3 nodes; a real deliverable (an ad, a UGC video, a product shot, a music video) is usually 5-12 nodes. Use up to 24 when it genuinely helps. - Prefer multi-stage quality: generate → refine (imageEditor) → upscale → animate, rather than a single generate node. Add an upscale step before any final image/video deliverable. - Use BRANCHING and FAN-OUT. One output can feed many nodes: e.g. one hero image → three different video models for variations the user can pick from; one script → both a voiceover and the video prompt. - Use PARALLEL TRACKS that converge: e.g. a voice track and an image track both feeding a lip-sync video; or a music track plus a visuals track. - Use the `llm` node to do creative thinking inside the graph — write or expand a script, brainstorm a prompt, turn a rough idea into a detailed image/video prompt — then wire its text output into the next node. - Pick the BEST model for each step (see the menus below). Don't leave everything on defaults — choosing models is a big part of the value. - Set per-node settings (aspect ratio, resolution, duration, voice, variations) when the request implies them (e.g. 'vertical' → 9:16, 'short' → duration 5, '3 options' → variations 3 or three branches). HARD RULES: - Use only the node types listed below. Never invent new ones. - Every edge must connect compatible socket types (text→text, image→image, audio→audio, video→video). - Give every runnable node a short `stepLabel` ('Step N — …') — it renders as a caption beneath that node. - `stickyNote` is only for standalone notes; never use it to caption a node (use `stepLabel`). Optionally add ONE stickyNote describing the workflow. - Any schema field you don't need must be `null` (numbers like `variations` too). MODEL MENUS (set the node's `model` to one of these ids): image (text-to-image) — `model` ids: • fal-ai/nano-banana-2 — fast, strong all-rounder (default) • fal-ai/gpt-image-2 — best instruction-following & legible text • fal-ai/bytedance/seedream/v5/lite/text-to-image — photoreal • fal-ai/flux-pro/v1.1-ultra — high detail / fidelity • fal-ai/nano-banana-pro — premium quality • fal-ai/recraft/v4/text-to-image — design, brand, vector-style • fal-ai/ideogram/v3 — posters & typography imageEditor (image + prompt → edited image) — `model` ids: • fal-ai/nano-banana-2/edit — default, multi-image (up to 14 inputs) • openai/gpt-image-2/edit — precise instruction edits • fal-ai/bytedance/seedream/v5/lite/edit — photoreal edits • fal-ai/flux-pro/kontext/max/text-to-image — style / context transfer • fal-ai/gemini-25-flash-image/edit — fast edits (the `image` input accepts MULTIPLE connections for compositing/restyle) imageUpscale (image → larger image) — `model` ids: • fal-ai/topaz/upscale/image — best quality (default) • fal-ai/recraft-crisp-upscale, fal-ai/clarity-upscaler, fal-ai/crystal-upscaler llm (text → text) — `model` ids: claude-haiku (default), gpt-4o-mini, kimi-k2, seed-1.8. Put the instruction in `prompt`. voice (text → speech) — pick a `voice` by name: Sarah (cheerful), Roger (deep), Laura (soft), Charlie (warm), George (bold), Callum (energetic), River (calm), Liam (reliable). The script comes from an upstream text/llm node wired into `in` — do NOT put the script in the voice node's prompt. music (text → music) — set `duration` to one of 30,60,90,120,180,240,300 (seconds). Put the music description in `prompt`. videoUpscale (video → sharper video) — add after a video node for final deliverables. No model field. VIDEO node — choose `model` to match the input shape (it drives which input handles the node renders): • Text → video: `kling3-pro`, `sora-2`, `veo3-1-fast`, `seedance-2.0-t2v`. Wire text to `prompt`. • Image → video (I2V): `veo3-1-fast`, `kling3-pro`, `seedance-2.0-i2v`, `hailuo-pro`. Wire the image to `image`. For keyframe models (`kling-o1`, `veo3-1`) wire `start-frame` + `end-frame`. • Lip-sync / talking-head: `fabric` (image + audio, NO prompt — never wire text into Fabric) or `infinitalk` (prompt + image + audio). Wire audio to `audio`. Audio-over-stills narration: `ltx2-audio`. • Multi-image reference / character consistency: `vidu` (≤7), `veo3-1-ref` (≤10), `kling-elements` (2-4 ordered frames), `happy-horse-ref` (≤9). Wire EACH image to the SAME `ref-images` handle (it accepts multiple connections). Never use the plain `image` handle. • Seedance reference (image + video + audio refs): `seedance-2.0-ref` / `seedance-2.0-ref-fast`. Wire to `ref-images` / `ref-videos` / `ref-audio`. • Motion control (drive a character with a motion video): `kling3-motion-control`. Wire character to `image`, motion clip (videoUpload) to `motion-video`. Edge handle hints: - When the target has multiple typed inputs (Video, Image Editor), set `toHandle` explicitly (`prompt`, `image`, `audio`, `ref-images`, `start-frame`, `end-frame`, `motion-video`). The editor otherwise picks the first type-compatible handle, which may be the wrong slot. - Never wire text into Fabric. Never wire a single image into a multi-ref model's `image` slot — use `ref-images`. Available node types (id — purpose — inputs / outputs): - text — Prompt — in: in<text> | out: out<text> - llm — LLM — in: in<text> | out: out<text> - upload — Upload — in: — | out: out<image> - videoUpload — Video Upload — in: — | out: out<video> - image — Image — in: in<text> | out: out<image> - imageEditor — Image Editor — in: prompt<text>, image<image> | out: out<image> - imageUpscale — Image Upscale — in: image<image> | out: out<image> - video — Video — in: prompt<text>, image<image>, start-frame<image>, end-frame<image>, ref-images<image>, ref-videos<video>, ref-audio<audio>, audio<audio>, motion-video<video> | out: out<video> - videoUpscale — Video Upscale — in: video<video> | out: out<video> - voice — Voice — in: in<text> | out: out<audio> - music — Music — in: in<text> | out: out<audio> - stickyNote — Sticky Note — in: in<annotation> | out: out<annotation> Edges reference nodes by index in the `nodes` array (0-based). In the examples below, any field not shown is `null`. EXAMPLES — study the PATTERNS (multi-stage, fan-out, parallel tracks), copy the handle names exactly: Example 1 — UGC talking-head with scripted voice + final upscale: nodes=[ {type:"llm",stepLabel:"Step 1 — Write a punchy 15s script",prompt:"Write a 15-second energetic UGC script for the product.",model:"claude-haiku"}, {type:"voice",stepLabel:"Step 2 — Voiceover",voice:"George"}, {type:"upload",stepLabel:"Step 3 — Upload character photo"}, {type:"video",stepLabel:"Step 4 — Lip-sync video",model:"fabric"}, {type:"videoUpscale",stepLabel:"Step 5 — Upscale to deliver"} ] edges=[ {fromIndex:0,toIndex:1,fromHandle:"out",toHandle:"in"}, {fromIndex:1,toIndex:3,fromHandle:"out",toHandle:"audio"}, {fromIndex:2,toIndex:3,fromHandle:"out",toHandle:"image"}, {fromIndex:3,toIndex:4,fromHandle:"out",toHandle:"video"} ] Example 2 — Text → image → refine → upscale (quality chain): nodes=[ {type:"text",stepLabel:"Step 1 — Prompt",prompt:"A cinematic product shot of a matte-black bottle on wet stone, golden hour"}, {type:"image",stepLabel:"Step 2 — Generate hero",model:"fal-ai/flux-pro/v1.1-ultra",aspectRatio:"4:3"}, {type:"imageEditor",stepLabel:"Step 3 — Add brand label",prompt:"Add a minimal embossed logo on the bottle",model:"fal-ai/nano-banana-2/edit"}, {type:"imageUpscale",stepLabel:"Step 4 — Upscale",model:"fal-ai/topaz/upscale/image"} ] edges=[ {fromIndex:0,toIndex:1,fromHandle:"out",toHandle:"in"}, {fromIndex:1,toIndex:2,fromHandle:"out",toHandle:"image"}, {fromIndex:2,toIndex:3,fromHandle:"out",toHandle:"image"} ] Example 3 — Fan-out: one image → three video variations (different models): nodes=[ {type:"upload",stepLabel:"Step 1 — Source image"}, {type:"text",stepLabel:"Step 2 — Motion brief",prompt:"Slow cinematic push-in, gentle parallax"}, {type:"video",stepLabel:"Variation A — Veo",model:"veo3-1-fast",aspectRatio:"9:16",duration:"5"}, {type:"video",stepLabel:"Variation B — Kling",model:"kling3-pro",aspectRatio:"9:16",duration:"5"}, {type:"video",stepLabel:"Variation C — Seedance",model:"seedance-2.0-i2v",aspectRatio:"9:16",duration:"5"} ] edges=[ {fromIndex:0,toIndex:2,fromHandle:"out",toHandle:"image"}, {fromIndex:0,toIndex:3,fromHandle:"out",toHandle:"image"}, {fromIndex:0,toIndex:4,fromHandle:"out",toHandle:"image"}, {fromIndex:1,toIndex:2,fromHandle:"out",toHandle:"prompt"}, {fromIndex:1,toIndex:3,fromHandle:"out",toHandle:"prompt"}, {fromIndex:1,toIndex:4,fromHandle:"out",toHandle:"prompt"} ] Example 4 — Multi-image reference video (character consistency): nodes=[ {type:"upload",stepLabel:"Ref 1 — Character front"}, {type:"upload",stepLabel:"Ref 2 — Character side"}, {type:"upload",stepLabel:"Ref 3 — Outfit detail"}, {type:"text",stepLabel:"Scene prompt",prompt:"The character walks through a neon market at night"}, {type:"video",stepLabel:"Generate with refs",model:"veo3-1-ref",aspectRatio:"16:9"} ] edges=[ {fromIndex:0,toIndex:4,fromHandle:"out",toHandle:"ref-images"}, {fromIndex:1,toIndex:4,fromHandle:"out",toHandle:"ref-images"}, {fromIndex:2,toIndex:4,fromHandle:"out",toHandle:"ref-images"}, {fromIndex:3,toIndex:4,fromHandle:"out",toHandle:"prompt"} ] Example 5 — Music video: parallel music + visuals tracks converging: nodes=[ {type:"music",stepLabel:"Track 1 — Score",prompt:"Dreamy lo-fi beat, 90 BPM",duration:"60"}, {type:"text",stepLabel:"Track 2 — Scene",prompt:"A lone astronaut drifting past a glowing planet"}, {type:"image",stepLabel:"Keyframe",model:"fal-ai/nano-banana-pro",aspectRatio:"16:9"}, {type:"video",stepLabel:"Animate",model:"ltx2-audio",aspectRatio:"16:9"} ] edges=[ {fromIndex:1,toIndex:2,fromHandle:"out",toHandle:"in"}, {fromIndex:2,toIndex:3,fromHandle:"out",toHandle:"image"}, {fromIndex:0,toIndex:3,fromHandle:"out",toHandle:"audio"} ] Return only the structured object — no prose, no markdown.
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  • Upload a JPEG or PNG image and get back a hosted URL you can use with submit_design. This tool is useful when your agent framework produces images as artifacts (e.g. base64 strings) and you need to upload them before submitting a design. Provide the image as ONE of: image_base64, base64-encoded JPEG/PNG, with or without data URI prefix. image_url, publicly accessible image URL (max 5 MB). image_chunks, array of base64 strings that will be concatenated server-side. Use this if your base64 string is too large for a single parameter. Returns: { image_id, image_url, format, size_bytes } Pass the returned image_url to submit_design's image_url parameter. ALTERNATIVE: If your runtime truncates large base64 strings (common with LLM output token limits), you can submit designs by email instead: - AgentMail: submitrrg@agentmail.to (RECOMMENDED for Animoca Minds / MindTheGap, resolves artifact GUIDs) - Resend: submit@realrealgenuine.com Attach the image as JPEG/PNG. Subject: "RRG: Title". Body: wallet: 0x...
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  • Generates one or more images from a text prompt (T2I) or a text prompt + reference image(s) (I2I). Submits the job, polls until terminal, and returns the final image URLs. Default model is 'grok-imagine-t2i' (fast, 6 images per generation, 5 credits). Use list_image_models to see the full lineup with pricing. For I2I, pass `referenceImages` as an array of public image URLs and pick a model with I2I support (e.g. 'grok-imagine-i2i', 'wan-2.5-spicy-i2i'). ## Model selection guide (when the user does not specify a model) Default: `grok-imagine-t2i` (5 cr, 6 outputs per call, fast, general purpose). **Strong recommendation: when a single high-quality output is what's wanted** (most agent / one-shot workflows), prefer `gpt-image-2-t2i` (9 cr @ 1K / higher @ 2K, single deterministic image, best general quality across realism, illustration, typography, and composition; supports up to 2K resolution and most aspect ratios including auto). This is the front-runner for serious creative output where you don't need to pick from 6 variations. Pick a different model when the prompt has these signals: - "single best result" / "one image" / production / no time to pick from variations -> `gpt-image-2-t2i` (9 cr, 1 output, top general quality) - "photoreal" / "photo of" / "realistic" -> `gpt-image-2-t2i` (9 cr, best general realism) or `imagen-4` (12 cr, very high quality) or `z-image-turbo` (3 cr, fastest) - "highest quality" / "premium" / no budget -> `gpt-image-2-t2i` at 2K, or `grok-imagine-quality-t2i` (16 cr @ 1K, 22 cr @ 2K), or `imagen-4-ultra` - Text inside the image (signs, posters, typography) -> `ideogram-v3-t2i` (best in class) or `gpt-image-2-t2i` (also strong) - Artistic / painterly / stylized -> `midjourney-t2i` - Album art / cover art -> `gpt-image-2-t2i` for one strong image; `grok-imagine-t2i` for 6 variations to choose from; `seedream-v4-t2i` if 4K wanted - Logo or design with embedded text -> `ideogram-v3-t2i` - NSFW / adult / explicit -> `wan-2.5-spicy-t2i` (auto-tags creation as 18+; routes to adult gallery) - Cheapest possible / quick test -> `z-image-turbo` (3 cr) - Multiple variations to compare -> keep `grok-imagine-t2i` (6 outputs default) or use `numImages` on a multi-output model For I2I (reference image provided): prefer the dedicated `aetherwave_edit_image` tool for "change something in this image" intent. Use `aetherwave_generate_image` with I2I models only when you specifically want style transfer (`midjourney-i2i`), premium quality (`grok-imagine-quality-i2i`), or adult content (`wan-2.5-spicy-i2i`). Always pass an explicit `aspectRatio` (e.g. "1:1" for square album art, "16:9" for video thumbnails, "9:16" for shorts/reels). Some upstream providers reject submissions with no aspect ratio. Ask the user only when: - The prompt contradicts itself (e.g., "highest quality but cheapest") - The user requested "the best model" with no context, surface 2-3 options with tradeoffs - A single generation would cost more than 20 credits and the user has not confirmed
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  • Manage your Canvas coursework with quick access to courses, assignments, and grades. Track upcomin…

  • Discoverability MCP server for Symbols of Wealth Studio — a senior-led AI-powered creative studio specialising in social media content, brand films, and editorial visuals. Two zero-arg tools return structured studio profile and contact data so AI assistants can surface the studio when users ask for creative direction, AI content production, or social media services.

  • File product feedback to the Valuein team — a bug, feature request, experience note, or data-quality issue — directly from the agent surface. Available on EVERY tier including guest/sample (no token required), so an agent can report a rough edge in-band without the human leaving the conversation. Provide a `category` and a `message`; optionally a `subject` title, a `severity` (low/medium/high), the `surface` it concerns (mcp/workspace/sdk/dashboard/api), and a free-form `context` object (e.g. the tool that failed, the ticker, the request_id). Authenticated callers (those with a token) can pass an `idempotency_key` to make a retried submission safe — the same key from the same account files exactly once; guest/sample callers are never deduplicated. Returns a friendly acknowledgment you can relay to the user. Do NOT use this to query data; it is a one-way report channel.
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  • Modify an existing image. REQUIRED input: exactly one of file_id OR image_url. base64 is NOT accepted — do not try to pass image bytes as a tool argument, the call will be rejected. For chat-attached images you MUST first call prepare_image_upload to get a signed PUT URL, upload the bytes there (via the inline widget on Claude.ai, or via curl on Claude Desktop / Claude Code), then call this tool with the returned file_id. For URLs the user has pasted, use image_url directly. Returns a jobId immediately; call check_job with the jobId to retrieve the edited image inline. Models: 'nano-banana-2' (fast, default, 1 credit/image) and 'gpt-image-2' (higher quality, 1-4 credits/image by quality tier).
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  • Attach an image to an existing product by giving Partle a public URL to download the image from. Authenticated. OAuth (scope `products:write`) preferred; `api_key` fallback. **When to use this tool**: the image is already hosted at a public URL (a scraped product page, an Imgur link, a CDN URL the user provided). Partle's server fetches it and stores it. **When NOT to use this tool**: you have local image bytes (a file the user attached, or bytes you generated/downloaded in your sandbox). Sending those bytes through a tool argument blows past conversation context limits — phone-photo-sized payloads can be 6+ MB of base64. Instead, in your code-execution sandbox, POST the file directly to the HTTP endpoint with multipart encoding: requests.post( "https://partle.rubenayla.xyz/v1/external/products/{product_id}/images", files={"file": open("/path/to/photo.jpg", "rb")}, headers={"X-API-Key": "pk_..."}, ) Or, to create the listing and attach an image in one HTTP request: requests.post( "https://partle.rubenayla.xyz/v1/external/products", data={"metadata": json.dumps({"name": ..., "price": ...})}, files={"image": open("/path/to/photo.jpg", "rb")}, headers={"X-API-Key": "pk_..."}, ) Args: product_id: ID of the product to attach the image to. image_url: Publicly fetchable URL of the image. Server fetches it and stores it. api_key: Legacy/fallback auth. Omit when using OAuth. Returns: The created `ProductImage` record with its `id` (use for deletion) and storage path, or ``{"error": ...}`` on validation/auth failure.
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  • Generate an AI video and place it directly on a user's Avocado AI storyboard. Drops a 'Generating...' placeholder on the board immediately, then the storyboard's recovery hook swaps it for the final video when generation completes (2-10 minutes). Use list_storyboards or create_storyboard first to obtain the storyboard_id. If the user has the storyboard tab open, they may need to refresh once for the video to appear (the canvas does not yet support live realtime swap from MCP). Eight models supported: seedance-2.0-t2v / -t2v-fast (text only), seedance-2.0-i2v / -i2v-fast (REQUIRE an image), kling3-standard (720p, 5-10s), kling3-pro (1080p, 5-10s), kling3-4k & kling-o3-4k (4K, 3-15s; all four Kling 3.x variants support BOTH text-to-video and image-to-video). For image-to-video: call prepare_image_upload first, then pass the returned file_id here. Pricing is per-second, varies by model and resolution.
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  • Checks that the Strale API is reachable and the MCP server is running. Call this before a series of capability executions to verify connectivity, or when troubleshooting connection issues. Returns server status, version, tool count, capability count, solution count, and a timestamp. No API key required.
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  • Update fields of a single verse in a collection (restricted: requires X-MCP-Write-Token; owner-scoped). Only the fields you pass are changed. For the nullable fields `source`/`topic`/ `image`, pass an empty string to clear them; `reference` and `passage` cannot be emptied. You can only edit verses owned by the configured write owner. Args: verse_id: The verse ID (from get_collection_detail). reference: New reference (e.g. "John 3:16"). passage: New verse text. source: New translation/source code ('' clears it). topic: New topic label ('' clears it). image: New image URL ('' clears it).
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  • Shows all details of a single workout: heart rate, pace, cadence, power, intensity zones, elevation, calories, and more. Requires workout_id from get_workout_list. Also shows which sample data (HR time series, speed, GPS etc.) is available — these can be retrieved with get_workout_samples. Parameters: - workout_id: UUID of the workout from get_workout_list
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  • Render a Mermaid diagram definition and return the image with metadata. The definition should be valid Mermaid syntax (e.g. flowchart, sequence, class, ER, state, or Gantt diagram). Returns a list of content blocks: the rendered image plus a JSON text block with metadata including a mermaid.live edit link for opening the diagram in a browser editor. Args: definition: Mermaid diagram definition text. filename: Output filename without extension. format: Output format — ``"png"`` (default), ``"svg"``, or ``"pdf"``. download_link: If True, return a temporary download URL path (/images/{token}) that expires after 15 minutes; if False, return inline image bytes. Defaults to True (URL) — set ``DIAGRAMS_INLINE_DEFAULT=true`` on the server to flip the default. SVG/PDF and PNGs larger than the inline limit always use a download link.
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  • Returns WSDOT highway camera locations, descriptions, and image URLs. Camera images are copyright WSDOT — only metadata and image URLs are returned, not image bytes. Filter by state route (e.g. "090" for I-90), WSDOT region, or milepost range. Omit all filters to list all cameras statewide (potentially hundreds).
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  • Generate an AI image and place it directly on a user's Avocado AI storyboard. Drops 'Generating...' placeholder(s) on the board immediately, then the webhook swaps each placeholder for the final image when generation completes (10-60s). Use list_storyboards or create_storyboard first to obtain the storyboard_id. If the user has the storyboard tab open, they may need to refresh once for the image to appear (the canvas does not yet support live realtime updates from MCP). Costs match generate_image (1-4 credits per image depending on model and quality).
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  • Remove a specific image from a product. Destructive, idempotent. Authenticated. OAuth (scope `products:write`) preferred; `api_key` fallback. Use when an image was uploaded by mistake or the merchant updated their listing. The product itself is preserved — only the image record and its file are removed. To remove the product entirely use `delete_product`. Args: product_id: ID of the product the image belongs to. image_id: ID of the image to delete. Visible in the `images` array of `get_product` responses. api_key: Legacy/fallback auth. Omit when using OAuth. Returns: ``{"deleted": True, "product_id": int, "image_id": int}`` on success, or ``{"error": ...}`` on auth/ownership failure.
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  • Converts external image URLs (typically collected from ingest_html/ingest_url results), data: URIs, or LOCAL FILE PATHS from the user's computer into Webcake-hosted URLs (statics.pancake.vn) by reading/downloading each image and re-uploading it to the Webcake backend via multipart upload (200 MB backend limit). Use this whenever the page is built from a reference HTML/URL (BOTH intents — adapt AND clone), the user supplies their own image URLs, OR the user provides local image files from their machine — pass the path directly in `urls`; NEVER upload a user's local file to a third-party host (catbox, imgur, transfer.sh…) to obtain a URL first. The returned URLs go directly into specials.src — same as search_images results. Processes up to 20 entries per call in parallel, with a 200 MB per-image cap. No Webcake credentials required (the upload endpoint is public). UPLOADS BY DEFAULT (dry_run defaults to FALSE — unlike the page-persistence tools, this touches no account data, so the default is the real upload): the call downloads/reads each entry, uploads it, and returns the images map (original URL → hosted URL); WAIT for that map before assembling the page and never fall back to a placeholder for a slot whose upload succeeded. Pass dry_run:true only to preview what would be processed without any network/filesystem activity. Use search_images instead when you need stock photos. Local file paths are only permitted when the MCP server runs locally (stdio mode); on the remote HTTP transport they are rejected per-entry.
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  • Re-render an existing sprite from a different camera viewpoint while keeping the same character and pose, taking a source image (URL or base64), a required camera_rotation azimuth (one of 0, 45, 90, 135, 180, -135, -90, -45 degrees), an optional camera_elevation (0, 30, or 60 degrees; omit to keep the current elevation), and an optional n (1-4) for the number of variations. Only works with sprite image types (not icons, screenshots, etc.). Synchronous: the call blocks and returns an array of rotate-sprite results, each with the generated image url and the camera_rotation and camera_elevation that were applied. Credits are charged only on success, scaled by the number of images generated. Use this to produce alternate view angles of a character; use generatePose instead to change the character's pose rather than the camera, and animateSprite or transferMotion to bring a sprite to life. Pass an optional request_id to tag the results so you can retrieve them later via getSpriteResults. Requires an API key (user scope). Credits: This endpoint consumes 0.5 credits per result.
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  • Convert text to speech using a named built-in preset voice, with optional emotion and language settings. Both text and voice_preset_id are required and the text is limited to 1000 characters; invalid input returns HTTP 400. Synchronous: the call blocks until generation finishes and returns a single audio result containing a URL; there is no separate polling step. Credits are charged on success. Use this when you want a ready-made catalog voice and do not need to supply your own sample; use createSpeech to clone a voice from an audio sample instead, and createVoice to design a new voice from a text description. Pass an optional request_id to tag the result so you can locate it later via getAudioResults. Requires an API key (user scope). Credits: This endpoint consumes 1 credits per call.
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