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204,191 tools. Last updated 2026-06-14 22:53

"A microcontroller with image processing capabilities" 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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  • Executes a Strale capability by slug and returns the result. Use this when you need to perform any verification, validation, lookup, or data extraction from the 271-capability registry. Call strale_search first to find the right slug and required input fields. Returns a result object with the capability output, quality score (SQS), latency, price charged, and data provenance. Five free capabilities work without an API key (10/day limit). Paid capabilities debit from the wallet — check strale_balance first for high-value calls.
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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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  • Recover detail from camera-shake and accidental motion blur. NAFNet (ECCV 2022, SOTA on GoPro/SIDD benchmarks). Best for: handheld shake, bumped camera, whole-frame uniform blur. NOT effective for: intentional panning blur, bokeh/depth-of-field, or artistic motion effects. Also supports denoising (grainy/noisy photos). 20 sats per image (~2 min processing), pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='deblur_image'.
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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 (both 1 credit/image): 'nano-banana-2' (fast, default) and 'gpt-image-2' (higher quality).
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  • 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.
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  • Image processing for AI agents. Resize, convert, compress, and pipeline images.

  • Free oncology data (research, trials, FDA approvals, news) plus IBM MAMMAL biomedical predictions.

  • 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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  • Classify image safety (normal / suggestive / explicit). Falcons.ai NSFW detection — 100x cheaper and faster than asking an LLM. Returns classification label and boolean is_nsfw flag. Essential for content moderation pipelines. 2 sats per image, pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='detect_nsfw'.
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  • Start an async rank of multiple candidates against a job description (8 credits). Returns task_id and analysis_id. Poll with careerproof_task_status, then fetch result with careerproof_task_result (result_type='fit_rank'). Requires context_id from atlas_list_contexts, candidate_ids from atlas_list_candidates (minimum 2), and jd_text. For async batch processing with more detail, use atlas_start_jd_fit_batch instead.
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  • Remove background from any image, returning transparent PNG. Uses BiRefNet (state-of-the-art, Papers with Code — Sm 0.901 on DIS5K). Handles hair, fur, glass, transparency, and complex edges. Stable endpoint — model upgrades automatically as SOTA evolves. 5 sats per image, pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='remove_background'.
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  • Edit an image with natural language instructions. Uses Nano Banana 2 — understands context, handles object addition/removal, style transfer, and inpainting. Returns JSON with image URL. Resolution-tiered pricing: 1K=200 sats, 2K=300 sats, 4K=450 sats. Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='edit_image' and resolution param.
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  • Check the processing status of an uploaded paper. Poll this tool after uploading a PDF until status is 'Ready' before calling get_variable_relationships. Args: file_id: The file_id returned by the /upload endpoint. authorization: Optional. API key as 'Bearer hk_...' or 'hk_...'. Returns: { "status": "Processing" | "Ready" | "Empty" | "Ineligible" | "Pending", "edges_count": int, "variables_count": int }
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  • 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
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  • 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).
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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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  • Use this whenever a user asks how many posts were published today, yesterday, this week, or in another date range, or asks what is queued/processing after publishing. This counts actual published delivery receipts separately from queued or processing posts, so do not describe queued posts as published.
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  • Get report status and metadata (without PDF). Returns status (pending/processing/completed/failed), title, type, inputs, and summary. This is the polling tool for ceevee_generate_report — call every 30 seconds, up to 40 times (20 min max). When status='completed', download PDF with ceevee_download_report(report_id). If status='failed', relay error_message. If still processing after 40 polls, stop and give the user the report_id to check later. Free.
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  • Search packs by keyword across names, descriptions, and tools (e.g., 'weather', 'translate'). Returns matching packs with details. Use to find specific capabilities.
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  • Get a random cat image matching a specific tag (e.g., 'orange', 'cute', 'sleepy'). Returns image URL, cat ID, and tags.
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  • Scrape Docker Hub image page with tag history, dockerfile signals. Heavier than lookup/dockerhub. Use for supply-chain audits. Example call: {"image": "library/nginx"} Cost: $0.005–$0.05 USDC on Base per call.
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