Skip to main content
Glama
214,458 tools. Last updated 2026-06-19 22:06

"A tool for image recognition" 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.)
    Connector
  • 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.
    Connector
  • 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.
    Connector
  • 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).
    Connector
  • 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.
    Connector
  • Mint a one-shot signed upload URL for a product you own. Authenticated. OAuth (scope `products:write`) preferred; `api_key` fallback. Use this when you have **local image bytes** (a file the user attached, bytes you generated/downloaded in your sandbox) and you want to attach them to a product that already exists. Common cases: - `create_product` returned 409 (duplicate name) — the listing already exists; this tool gives you an upload URL for it without creating anything new. - You're adding a 2nd, 3rd, … photo to a product. The returned URL is valid for ~15 min, single product, signed with your authenticated identity. From your sandbox, do **one PUT**: requests.put(result["upload_url"], data=open("/path/to/photo.jpg", "rb").read(), headers={"Content-Type": "image/jpeg"}) No auth header on that PUT — the URL is the credential. If you have a public URL (not local bytes), use `upload_product_image(product_id, image_url=...)` instead. Args: product_id: Product to attach the future image to. You must own it. api_key: Legacy/fallback auth. Omit when using OAuth. Returns: ``{"upload_url": str, "upload_expires_in": int}``, or ``{"error": ...}`` on auth/ownership failure.
    Connector

Matching MCP Servers

Matching MCP Connectors

  • 斯特丹STERDAN天猫旗舰店产品咨询MCP Server。洛阳30年源头工厂,高端钢制办公家具,1374个SKU,涵盖保密柜、更衣柜、公寓床、货架、快递柜。BIFMA认证,出口35+国家。8个工具:产品目录查询、场景推荐、认证资质、采购政策、维护指南等。

  • 台灣勞保、健保、勞退、職災與二代健保補充保費試算,含薪資扣繳、破月與勞保老年給付。資料取自主管機關公告,對官方範例逐位元驗證。

  • Copy an image that already exists on one output onto another cell, instant and free (no regeneration, no credits). Use this when the user wants 'the same image' on a second surface ('use the LinkedIn image on X', 'same picture on the newsletter') instead of niche_render_image_card (which generates a new image and costs credits). Both cells must already exist on the session (add the target via niche_add_output first if needed) and the source must have a rendered image. Copies the source's static_urls onto the target so it publishes with that image. Idempotent: source==target is a no-op.
    Connector
  • 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...
    Connector
  • Regenerate one image inside a specific section of a WebZum site. Creates a new version with a freshly AI-generated image for that section and reassembles. Use the optional userMessage to steer the new image — "show a wider shot", "change the angle", "make it sunset lighting", etc. Required: businessId, versionId, sectionId. Returns { versionId, status: 'completed' | 'in_progress', ...extra }. If status is 'in_progress', poll get_site_status with the returned versionId every 5-10s until isComplete is true. Concurrency: edits on the same businessId MUST be serial. Never fire parallel edit calls on the same site.
    Connector
  • Render a project's current model server-side and return it as an inline image so you can SEE what you built. Use this after open_in_studio (or any /p/<slug> link): call with that `slug` to inspect whether the build looks right. CRITICAL — the image is rendered from the MODEL on the server; it does NOT reflect the user's Studio camera, zoom, or screen. NEVER ask the user to rotate, zoom, pan, move the camera, close a slider, or change their view to help you see — you cannot affect their screen and it cannot affect this render. To see a different angle, call this tool again with a different `view`. By DEFAULT (omit `view`, or `view:"all"`) it returns a CONTACT SHEET of all six canonical views in one labeled image — a 3×2 grid, top row [iso, front, right], bottom row [back, left, top] — so you can judge the model from every side regardless of its orientation (e.g. to find which side has the doors). Pass a single `view` (iso/front/back/left/right/top) for one large render of that angle. DETERMINISTIC: the same model + view always returns the same bytes — identical bytes are NOT a stale/lagging snapshot. If you changed the model, push it with open_in_studio FIRST, then re-render to see the change. The image is always current and never a blank capture. Colors and shading match Studio (same palette / base-material color). The slug is the capability: no OAuth for public/unlisted; private projects require the owner signed in. The PNG is base64-inlined as a real image block by default; pass `paths_only: true` for metadata only. No renderable geometry or a mesh failure → { ok: false, error, hint }, never a blank image.
    Connector
  • MANDATORY first step whenever the user attached an image in chat (or pointed at a local file on disk) and wants edit_image or image-to-video generation. Returns a signed PUT URL plus a file_id. After this tool: either (a) the inline upload widget will let the user drop the file and auto-continue (Claude.ai web), or (b) you run a curl PUT yourself if you have shell access (Claude Desktop / Claude Code) — the response text contains a ready-to-run curl command. Then call edit_image or generate_video with file_id=<returned id>. edit_image and generate_video do NOT accept base64 — calling them with raw image bytes WILL fail. This tool is the only working path for chat attachments. Set `purpose` to 'edit' or 'video' so the upload widget points the user at the right downstream tool.
    Connector
  • 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).
    Connector
  • Retrieve the final output of a completed async job. Call ONLY after check_job_status returns status='completed' — calling on a non-completed job returns an error. Returns JSON whose shape depends on jobType: video/video-image → { videoUrl, duration }; image-3d → { modelUrl } (GLB format); transcription → { text, language, segments }; epub-audiobook → { audioUrl, chapters }; ai-call → { transcript, duration, summary }. All URLs are temporary (valid ~1 hour) — download immediately. This tool is free and does not require payment. Do NOT use for synchronous tools — those return results directly.
    Connector
  • 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).
    Connector
  • Upload an image and return the hosted image record. ``data`` must be the image bytes encoded as standard base64 (RFC 4648). Accepted image formats are PNG, JPEG, WebP, and GIF. ``visibility`` controls who can access the served URL: ``"public"`` makes it accessible to anyone with the link; ``"private"`` (default) requires the owner's credentials. Accepted values: ``"public"``, ``"private"``. ``ttl_seconds`` sets an expiry relative to now (positive integer). Omit to create a permanent image. Returns: ``{id, token, url, visibility, expires_at, size_bytes, content_type}``.
    Connector
  • 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.
    Connector
  • Restore and enhance faces in an image using GFPGAN. Detects all faces via RetinaFace, restores quality (fixes blur, noise, compression artifacts), and pastes them back. Optionally enhances the background using Real-ESRGAN. GPU-accelerated, sub-3s latency. Args: image_base64: Base64-encoded image data containing faces (PNG, JPEG, WebP). upscale: Output upscale factor -- 1 to 4 (default: 2). enhance_background: Whether to enhance background with Real-ESRGAN (default: true). Returns: dict with keys: - image (str): Base64-encoded restored image - format (str): Output image format - width (int): Output width - height (int): Output height - upscale (int): Scale factor applied - processing_time_ms (float): Processing time in milliseconds
    Connector
  • Render a saved template with variable substitutions to produce an image or PDF. Templates can be FabricJS canvas designs or HTML — both are rendered the same way via this endpoint. WORKFLOW: 1) Use pictify_list_templates to find a template, 2) Use pictify_get_template_variables to discover its variables, 3) Call this tool with the variable values. Common use cases: OG images with dynamic titles, personalized social cards, product images with prices/descriptions, event banners with speaker info. For rendering the same template with many variable sets, use pictify_batch_render. Returns the hosted image URL (CDN-backed).
    Connector
  • Present an image upload widget to the user. WHEN TO CALL: Any Glance flow that requires a user-provided image (visual search, outfit inspiration, style matching, product comparison, or any image-based analysis) and the user has NOT already uploaded one in their message. Do NOT ask the user to attach an image manually — call this tool instead to open the upload widget. WHAT TO DO AFTER: Once the user confirms the upload, immediately call `get_uploaded_image` to retrieve the image, analyse it, and then continue the flow based on what you see in the image.
    Connector
  • Retrieve the image the user most recently uploaded via the `upload_image` tool as an image block for analysis. WHEN TO CALL: Immediately after the user confirms their upload (message says 'Image uploaded successfully'). WHAT TO DO AFTER: Analyse the image and continue the original flow — describe what you see, extract style/colour/product details, and use that information to fulfil the user's request. Never wait for further instruction before analysing.
    Connector