"Generating an image for a cursor design or customization" matching MCP tools:

• get_knowledge_version

  marmo-ui

  Returns the MCP knowledge version: gitSha, indexedAt, componentCount, patternCount, uptimeSeconds. Call this ONCE per session before generating UI code so you know how fresh the design-system data is. Cheap to call. If gitSha is "unknown" or indexedAt is far in the past, surface that to the user before relying on the data.

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• upload_product_image

  Partle

  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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• perspective_respond

  Perspective AI

  Sends the user's answer to a follow-up question raised by the design agent during perspective creation, then re-runs the design step. Returns a new pending job_id; long-poll perspective_await_job for the next terminal state. Behavior: - Appends the user's reply to the design conversation and kicks off another design pass. Each call starts another pass. - ONLY valid while the perspective is in DRAFT status. Errors with "This perspective already has an outline. Use the update tool to make changes." otherwise. - Errors when the perspective is not found or you do not have access. - Returns "pending" immediately. perspective_await_job resolves to "ready" (outline generated) or "needs_input" (another follow-up — call this tool again). When to use this tool: - perspective_await_job returned status "needs_input" with a follow_up_question and you have the user's reply. - Continuing the design dialogue before any outline is generated. When NOT to use this tool: - The perspective already has an outline — use perspective_update for revisions. - Starting a new perspective — use perspective_create. - Polling a previously-enqueued job — use perspective_await_job.

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• upload_image

  Real Real Genuine

  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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• firmaradar_start_aml_report

  Firmaradar

  Start generating an AML risk report ASYNCHRONOUSLY for a Norwegian company. Returns immediately with a report_id and status 'pending' — the report is built in the background. Poll `get_aml_report` with the report_id until status is 'done' (then read score/level/factors) or 'failed'. Use this instead of `get_aml_score` for large/complex ownership structures that may otherwise time out, or to start many screenings in parallel. Generates an auditable report stored for 60 months per Hvitvaskingsloven §35.

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• aetherwave_generate_image

  AetherWave Studio

  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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• get_knowledge_version

  Marmo UI MCP

  Returns the MCP knowledge version: gitSha, indexedAt, componentCount, patternCount, uptimeSeconds. Call this ONCE per session before generating UI code so you know how fresh the design-system data is. Cheap to call. If gitSha is "unknown" or indexedAt is far in the past, surface that to the user before relying on the data.

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• generate_image

  BananaBanana Image & Video Generation

  Start an AI image generation (Google Nano Banana family). Charges the account balance immediately and returns a job_id — poll get_result for the finished image URLs. Typical completion: 10–60 seconds. Costs $0.03–$0.20 per image depending on model and resolution (see list_models). Failed generations are automatically refunded. Generating several images at once (number_of_images > 1) is a batch: the first call returns a price quote and charges nothing — repeat the call with confirm_cost set to the quoted amount to start. Example: {"prompt": "studio photo of a ceramic mug on linen, soft daylight", "model": "nano-banana-2", "aspect_ratio": "4:5", "resolution": "1024"}

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• get_latest_releases

  Releases

  Get the most recent releases, optionally filtered by product or organization. Excludes prereleases (canaries / alphas / betas / RCs) by default — pass `include_prereleases: true` to include them. Cursor-paginated: pass `limit` for slice size (default 10), `cursor` to continue from a prior call. The result's `_meta.pagination` carries `kind: 'cursor'`, `hasMore`, and `nextCursor` when more rows exist; the response text echoes `nextCursor` so an LLM caller can chain without parsing `_meta`. Cursors are stable under inserts — a release added between calls won't shift the slice.

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• get_collection_releases

  Releases

  Interleaved cross-org release feed for a collection — same shape as `get_latest_releases` but scoped to the collection's member orgs. Cursor-paginated: pass `limit` for slice size (default 20), `cursor` to continue from a prior call. The result's `_meta.pagination` carries `kind: 'cursor'`, `hasMore`, and `nextCursor` when more rows exist; the response text echoes `nextCursor` so an LLM caller can chain without parsing `_meta`. Cursors are stable under inserts.

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• agent_verify

  Colour Memory

  Verify that an AI-generated image actually used the colours specified in an agent_brief call. Supply the generated image (URL or base64) and the target palette from agent_brief colour_tokens. Returns a fidelity score 0-100, dE2000 distance per colour, match quality per colour (accurate/acceptable/drifted/ignored), and an overall verdict. Use after agent_brief + image generation to close the colour loop.

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• update_image

  Goodeye

  Update visibility, TTL, or the private view link of a hosted image. Caller must own it. ``visibility`` flips the access level: ``"public"`` or ``"private"``. Omit to leave unchanged. ``ttl_seconds`` and ``permanent`` are mutually exclusive. ``permanent`` clears the expiry so the image lives indefinitely. ``ttl_seconds`` sets a new expiry relative to now (positive integer). Omitting both leaves the current expiry unchanged. ``rotate_view_secret`` issues a fresh private view link and invalidates every link shared earlier for this image, so use it to un-share a private image. For a private image the returned ``url`` is the current view link. Returns the updated image record. Raises ``image_not_found`` (404) when the image is absent, expired, or owned by another user.

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• edit_image_to_flow

  Avocado AI

  Edit an existing image and place the result directly on a user's Avocado AI flow (the Flows Director). This is the flow-native edit: use it (NOT the plain edit_image) for ANY edit inside a flow, so the result lands in the user's Director Library. This MODIFIES ONE SPECIFIC existing image (file_id/image_url is required) — it does not generate new frames from a prompt. To generate a NEW image guided by one or more reference images (e.g. a storyboard beat that must keep the same cast/location consistent), use generate_image_to_flow with reference_image_urls instead. Drops a 'Generating...' tile immediately, then swaps in the edited image when ready (10-60s). REQUIRED: exactly one of file_id OR image_url (HTTPS). For chat-attached images call prepare_image_upload first, then pass the returned file_id. Models: 'nano-banana-2' (fast, default, 1 credit), 'nano-banana-2-lite' (fastest/cheapest, single-image touch-ups, 1 credit), and 'gpt-image-2' (higher quality, 1-4 credits by quality). To regenerate/retouch an EXISTING tile in place (same tile stays 'the' cast/location/beat reference — nothing downstream breaks) instead of creating a duplicate, pass replace_node_id (get it from a prior generate_image_to_flow/edit_image_to_flow response's nodeIds, or from list_flow_assets); typically image_url would then be that same tile's own current url.

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• upload_product_image

  Partle Marketplac

  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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• get_land_quick_score

  AcreLens

  Get a fast suitability score (0-100) for a US property without generating a full report. Call this when the user wants a quick go/no-go assessment or an initial screening before committing to a full analysis. Returns a single score with confidence level and one-sentence rationale. Consumes a partial (0.25) analysis credit from your AcreLens account.

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• design_session

  Colour Memory

  One-call compound tool. Submit a concept, medium, audience, and constraints — receive a complete design package: historically grounded palette, cultural narrative, commercial paint matches, WCAG accessibility check, illuminant behaviour, and a ready-made image generation prompt. Replaces chaining query_conceptual + palette_from_concept + colour_story + match_paint_system + accessibility_check + get_colour_metrics. Use when an AI agent or user needs a complete, deployable colour direction in a single call. Not for iterative refinement — use individual tools for that.

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• list_image_generators

  Goodeye

  List active (non-revoked) image generators owned by the caller. Platform-managed system generators (the standard, premium, and image-to-image tiers) are excluded; those are run-only and not listed. Cursor-based pagination mirrors ``list_verifiers``. Returns ``{items: [{generator_id, name, description, current_version, version_token, status, scope, created_at, updated_at}], next_cursor: str | null}``.

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• list_images

  Goodeye

  List hosted images owned by the caller, with optional filters. ``source`` filters by upload origin: ``"upload"`` for directly uploaded images, ``"generated"`` for images created via the image generation tools. Omit to return all sources. ``visibility`` filters by access level: ``"public"`` or ``"private"``. Omit to return both. Pagination: pass ``next_cursor`` from a previous response as ``cursor`` to retrieve the next page. Returns ``{items: [...], next_cursor: str | null}``.

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• data_query

  shiply

  Page records from an owned site's collection, newest-first. limit ≤ 200 (default 50). cursor from a previous response's nextCursor.

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• generate_deck_image

  tela

  Generate an image from a prompt and attach it to a deck page (editor+), ready for a bg:/image: slot. Returns the serve URL + a  snippet; reference it by path (don't regenerate on re-render). Read the imagery module first (deck_authoring_guide module="imagery"): most slides need NO image — use it for atmosphere/concept/focal only, reuse ONE background, write rich on-palette prompts, and prefer images raw. May be unavailable (503) if the instance hasn't configured image generation or AI is paused; generation can take from ~20s to a few minutes depending on the model.

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