⚡ ACTION: Generate images from text prompts using Stable Diffusion — supports style presets (anime, cinematic, pixel-art, photographic...), aspect ratios, negative prompts. Returns base64 PNG data URI. Powered by Stability AI
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742,046 tools. Updated 2026-11-04 12:11
"Tools or methods to generate anime-style images" matching MCP tools:
- 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: Optional API key (`pk_*`, generate at /account). Used when there is no OAuth token, and also when the OAuth token lacks the required scope — an explicitly passed key overrides an ambient token that is scoped too narrowly. An invalid or revoked token still fails regardless. Omit when using OAuth. Returns: ``{"deleted": True, "product_id": int, "image_id": int}`` on success, or ``{"error": ...}`` on auth/ownership failure.ConnectorDestructiveNo auth
- 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: Optional API key (`pk_*`, generate at /account). Used when there is no OAuth token, and also when the OAuth token lacks the required scope — an explicitly passed key overrides an ambient token that is scoped too narrowly. An invalid or revoked token still fails regardless. Omit when using OAuth. Returns: ``{"deleted": True, "product_id": int, "image_id": int}`` on success, or ``{"error": ...}`` on auth/ownership failure.ConnectorDestructiveNo auth
- Upload a base64-encoded file to a site's container. Use this for binary files (images, archives, fonts, etc.). For text files, prefer write_file(). Requires: API key with write scope. Args: slug: Site identifier path: Relative path including filename (e.g. "images/logo.png") content_b64: Base64-encoded file content Returns: {"success": true, "path": "images/logo.png", "size": 45678} Errors: VALIDATION_ERROR: Invalid base64 encoding FORBIDDEN: Protected system pathConnectorNo auth
- Capability manifest of the hosted runners: LAMMPS version, installed packages, and every style by category with gpu=true (KOKKOS-accelerated) or gpu=false (exists, but runs on the pod's CPU cores at the GPU rate). Default = compact summary; runner=lammps&full=true returns the whole style table. Use preflight_deck to check a specific deck.ConnectorNo auth
- Check whether still images of a project show the SAME character as its character sheet — before paid clips are made from them. Each image is compared against the sheet's detail and fullbody views. It judges identity (species, build, colouring, head shape, large markings), NOT style, light, pose, place or props — and deliberately NOT micro details like an ear notch or the number of fingers, which no image model holds reliably. Free. Runs in the BACKGROUND: returns a `job_id` with HTTP 202 — poll it with lab_get_job until status is done|failed; the result lists per image passt, sicherheit, abweichungen, begruendung (or error). Up to 16 images and frames together per call. Images must be completed images of this project (imported images are refused). To check frames of a GENERATED CLIP (an orbit turnaround, a lab_animate clip) pass clip_frames: the clip's video_id and the seconds to cut out — the frames are taken from the clip itself and compared the same way; a second beyond the clip's end fails only that frame. The sheet needs a reference image (lab_generate_character_reference).ConnectorOAuth
Matching MCP Servers
- AlicenseAqualityCmaintenanceEnables structured extraction of methods and reproducibility heuristics from academic papers, allowing AI agents to obtain metadata, full text, structured methods, code repository discovery, and a no-clone reproducibility verdict from a paper URL.835 PyPIMIT
- AlicenseNot gradedqualityDmaintenanceProvides MCP tool adapters for Bioconductor methods like limma, DESeq2, and fgsea, enabling statistical analysis of omics data through containerized R execution. It serves as a bridge between MCP clients and bioinformatics tools for reproducible research workflows.Apache 2.0
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Research methods for AI assistants: study design guidance, R and Python code, and citations
AI commerce intelligence for anime, industrial parts, replacements, sourcing and agent buying.
- Add and/or remove photos on one of the USER'S OWN existing listings (max 6 total). First call request_image_upload for each new image and upload the bytes, then pass the returned keys in `add`; a key with no uploaded file fails with image_not_uploaded. Adding any image re-triggers AI moderation — the listing returns to 'pending' until the new images are cleared. Removing images leaves a live listing live; on a pending or rejected listing it requests a new moderation verdict. Free (no credit).ConnectorNo auth
- Permanently delete a product listing and all its images. Destructive. Authenticated. OAuth (scope `products:write`) preferred; `api_key` fallback. Use only when the user explicitly asks to remove a listing they own. Cannot be undone — there is no soft-delete or trash bin. Idempotent: deleting a product that no longer exists returns an error, not duplicate side effects. Caller must own the product. Args: product_id: ID of the product to delete. Get from `get_my_products`. api_key: Optional API key (`pk_*`, generate at /account). Used when there is no OAuth token, and also when the OAuth token lacks the required scope — an explicitly passed key overrides an ambient token that is scoped too narrowly. An invalid or revoked token still fails regardless. Omit when using OAuth. Returns: ``{"deleted": True, "product_id": int}`` on success, or ``{"error": ...}`` on auth/ownership failure.ConnectorDestructiveNo auth
- Permanently delete a product listing and all its images. Destructive. Authenticated. OAuth (scope `products:write`) preferred; `api_key` fallback. Use only when the user explicitly asks to remove a listing they own. Cannot be undone — there is no soft-delete or trash bin. Idempotent: deleting a product that no longer exists returns an error, not duplicate side effects. Caller must own the product. Args: product_id: ID of the product to delete. Get from `get_my_products`. api_key: Optional API key (`pk_*`, generate at /account). Used when there is no OAuth token, and also when the OAuth token lacks the required scope — an explicitly passed key overrides an ambient token that is scoped too narrowly. An invalid or revoked token still fails regardless. Omit when using OAuth. Returns: ``{"deleted": True, "product_id": int}`` on success, or ``{"error": ...}`` on auth/ownership failure.ConnectorDestructiveNo auth
- Find an existing PROVEN strategy that matches a plain-English idea, so you can offer the user a choice — deploy the existing one, or generate a fresh custom one. Mirrors the quantifyme.ai landing experience: "Found <X> by @<author> (WR/PF) — Use it / Generate fresh". Use this when a user wants to browse for an existing strategy. A vague deploy request does NOT need it: one_shot(prompt=...) deploys the best curated preset or a fresh build. A community strategy is another user's code, so it deploys only after the user has seen its author and code (get_strategy_code with the match's community_id) and picked it. Present the match (if any) and ASK: • Use it → one_shot(community_id=<match.community_id>) — deploys the exact strategy they picked (no generation). • Generate fresh → one_shot(prompt="<their description>") — Claude writes a brand-new custom strategy for them. If there's no match, just offer to generate fresh. Args: description: the user's strategy idea in plain English (e.g. "buy EURUSD 15min when RSI < 30, sell when RSI > 70"). symbol: optional pair to constrain the match (EURUSD, USDJPY, GBPUSD, USDCHF, USDCAD, AUDUSD, NZDUSD). timeframe: optional granularity to constrain the match (1min/5min/15min/1h). Returns: dict with: - match: the best existing strategy, or null. When present: {community_id, title, username, wr, pf, ret, n_trades, symbol, timeframe}. Pass community_id to one_shot to deploy it unchanged. - description: echoed back — pass as one_shot(prompt=...) to generate fresh. - suggestion: a ready-to-show sentence offering the user the choice.ConnectorNo auth
- Low-level Telegram API (MTProto) invoke for methods not wrapped by other tools. Dangerous methods require allow_dangerous=true. Success: API result dict or normalized error. PII and credential-shaped fields (phone, access_hash) are dropped from a successful result by default; pass include_sensitive=true for the raw payload. A bare message id needs a chat binding: requests with no peer field (messages.GetMessages, messages.DeleteMessages) are refused, because a bare id resolves against an arbitrary dialog. Use channels.GetMessages or messages.GetHistory, which carry the binding. messages.GetHistory cannot address a forum topic (no thread_id/top_msg_id in the schema, and channels.GetHistory does not exist) -- use messages.Search with top_msg_id, or the high-level get_messages with reply_to_id. Full documentation: https://github.com/leshchenko1979/fast-mcp-telegram/blob/main/docs/Tools-Reference.mdConnectorDestructiveNo auth
- Low-level Telegram API (MTProto) invoke for methods not wrapped by other tools. Dangerous methods require allow_dangerous=true. Success: API result dict or normalized error. PII and credential-shaped fields (phone, access_hash) are dropped from a successful result by default; pass include_sensitive=true for the raw payload. A bare message id needs a chat binding: requests with no peer field (messages.GetMessages, messages.DeleteMessages) are refused, because a bare id resolves against an arbitrary dialog. Use channels.GetMessages or messages.GetHistory, which carry the binding. messages.GetHistory cannot address a forum topic (no thread_id/top_msg_id in the schema, and channels.GetHistory does not exist) -- use messages.Search with top_msg_id, or the high-level get_messages with reply_to_id. Full documentation: https://github.com/leshchenko1979/fast-mcp-telegram/blob/main/docs/Tools-Reference.mdConnectorDestructiveNo auth
- Explain how to use a public prompt_style from the RetroDiffusion API. Use this before create_inference if you are unsure whether a style expects `input_image`, supports per-inference `reference_images`, or whether you meant style-level `style_reference_images`.ConnectorNo auth
- Explain how to use a public prompt_style from the RetroDiffusion API. Use this before create_inference if you are unsure whether a style expects `input_image`, supports per-inference `reference_images`, or whether you meant style-level `style_reference_images`.ConnectorNo auth
- READ-ONLY: returns generated source code as text and writes nothing to disk, creates no project and runs no command. Generates an idiomatic @imqueue/rpc service (an IMQService subclass with @expose()d, JSDoc-typed methods) plus a bootstrap that starts it. Provide the methods you want, or omit them for a starter template. Any non-primitive parameter or return type also gets a types.ts with the required @classType()/@property() declarations — without those the generated client types it `any`, which compiles. Use create_service (local install only) if you want files actually written.ConnectorNo auth
- Generate a cohesive SET of custom images on one SOLID-COLOR background or a TRANSPARENT one, each one a separate isolated subject sharing one visual style (icons, logos, game assets, UI elements, sprite/asset packs; transparent for images that drop onto any backdrop). The style parameter says how everything is drawn; the subjects parameter says what to draw; the background parameter is a color or "transparent". The style can also come from reference images via the styleReferences parameter - alone, or best combined with the style text (text plus references holds a style tightest). Returns a zip download URL. Cost: 1 credit per call. When a solid color fits the user's use case, it is the faster choice. Run generation calls sequentially, never in parallel - only one generation runs at a time per API key. For composed, full-bleed pictures (scenes, hero images, banners, posters, environments), use generate_illustration_set instead. For several images of ONE character (the same mascot, creature, person, or product in different poses or moods), use generate_character_set instead. For many different ideas from one brief, each in its own style (logo ideas, mascot concepts), use generate_concept_set instead. For ONE logo shown many ways, use generate_logo_variations instead. Output formats: PNG (lossless), JPEG, or WebP; JPEG has no alpha channel, so a set delivered transparent is PNG or WebP. Images can be delivered at a fixed width and height, at one fixed axis with the other hugging each subject, or at their native resolution. Size and quality considerations: Leaving width and height unset delivers images at their native resolution, unscaled under relative sizing except that a subject far larger than the rest is shrunk (fill sizing scales each subject to its frame), which produces the highest quality results and is recommended when images will be post-processed, composited, or resized downstream. On a solid background, native output dimensions vary between generations and track the subject count - roughly 650-950px per side for small sets, down to roughly 400-650 at the full 16; fewer subjects means larger native images. Fixing width and height (e.g. 512 and 512) guarantees consistent dimensions across all images and generations but applies resampling which may soften fine details; fixing one axis lets each image keep its subject's own proportion on the other. IMPORTANT - style and subject description rules for best results: The style applies to every image in the set, so it is what keeps them visually consistent. Put HOW the images are drawn (technique, palette, surface treatment) in the style, and make each subject description only about WHAT that one subject is, not how it looks. A quick test for any phrase: is it WHAT the subject is, or HOW it is drawn? HOW belongs in the style, shared across the set. - Get the style and the subject descriptions right with the user before you call. When their request puts how an image is drawn, a background, or a scene into a subject description (or names the subjects to draw in the style rather than as separate entries in the subjects list), fix it as you compose the call: routine moves of shared technique into the style you can just make, but when a change drops or alters something they explicitly asked for, tell them what you are adjusting and why first. Each call costs a credit, so it is worth getting this right up front rather than spending one on a framed or scene-filled result. - The style describes the visual treatment of the images (e.g. 'watercolor', 'pixel art', 'stained glass'). It must NOT mention background color, image count, layout, or sizing. - Do not list the subjects to draw in the style (e.g. 'illustrations of a fox, an owl, and a deer'); the style is only the shared visual treatment, and the subjects belong in the subjects list, one per entry. A category or theme word is fine (e.g. 'insect illustration'). - Do not put background color or background descriptions in the style or subject descriptions. - Avoid framing the style as a type of painted canvas ('oil painting', 'acrylic painting', 'gouache painting', 'pastel painting'). These tend to produce each image as a rectangular framed canvas with its own colored background, rather than an isolated subject. Prefer 'illustration' or a specific technique: 'watercolor illustration', 'pen-and-ink sketch', 'ink wash', 'relief-etching', 'pastel drawing', 'woodblock print'. - Avoid color-field or atmospheric phrasings in the style ('luminous backgrounds of violet, rose, and gold', 'set against jewel-tone fields', 'dreamlike rainbow atmosphere'). These instruct the image model to fill each image with colored atmosphere, producing framed compositions rather than isolated subjects. Describe only the linework, palette, and technique of the subjects themselves. - Do not describe an aged, weathered, cracked, or textured surface, ground, wall, panel, or paper that the whole artwork sits on ('on aged wood', 'cracked fresco wall', 'aged parchment surface'); name the art tradition(s) or style(s) instead ('fresco-style illustration'). Texture that belongs to a subject's own material is fine ('a weathered bronze shield', 'a cracked ceramic vase'). - No captions, labels, or annotations. Text that is part of the depicted object is fine (e.g. 'STOP' on a stop sign, 'EXIT' on an exit sign). - No grid lines, borders, frames, or separators. - No overlapping or collage-style arrangements. - Do not connect the subjects to each other or give them shared physical elements: no wires, cords, chains, ropes, ribbons, vines, or threads running between subjects, no frame or banner they share, no phrasing like 'connected by' or 'strung together', and no single continuous line or tube forming multiple subjects. Each subject must be drawable in complete isolation; connections inside one subject (a chain on an amulet, laces on a boot) are fine. - No dramatic/long drop shadows (subtle shadows are fine). - Subject descriptions should describe WHAT to depict, not where to position it. - Each image is ONE isolated subject, not a scene. Describe the subject with its pose or action and anything it directly holds, rides, or interacts with, but not the surrounding setting, environment, landscape, or sky. For composed scenes (a figure set within an environment), use generate_illustration_set instead. - Do not use size words (large, tiny, small, etc.) on the overall image subject (e.g. 'a large elephant', 'a tiny mouse') - all images are produced at the same size. Size words on details within the image are fine (e.g. 'a plate with a small insignia'). - Maximum 16 images per generation. Do not put the image count in the style. - Subjects must be distinct: entries that differ only in case, punctuation, or spacing count as the same subject and the call is rejected. Explicit filenames must be distinct too (a different extension alone is not distinct). - The style must actually describe a visual style, and each subject must name a drawable subject; text that does not is rejected. - Style description max length: 500 characters. Subject description max length: 200 characters each. - Size: width and height are separate parameters, each 256 to 512 pixels when given. Both given is an exact box; one given fixes that axis and the other hugs each subject (so images in the set differ on it, and it may fall below 256); neither given delivers native resolution, which is also the path to larger images. - Sizing: relative (the default) keeps the sizes the model gave the subjects in relation to one another, one scale for the whole set; fill scales each subject on its own to fill its frame less the margin, the icon-set convention, giving up relative size and enlarging subjects smaller than the frame (the result says by how much). Both can be changed later with edit_set. - Icons: the subject count sets how large a batch's icons can later be exported with export_set, crisp at every density: about a 136px base size with 13 to 16 subjects, 160 with 10 to 12, 180 with 7 to 9, 192 with 5 or 6, 256 with 4 or fewer. Every generation result states its batch's own crisp base size. - If the style check returns a suggested cleanup, show the user the specific changes and get their confirmation, then resubmit the approved prompt with validation set to "skip" so it generates exactly as approved (resubmitting without "skip" re-runs the check and may return further suggestions). See the validation parameter for when to use "skip" and "auto-apply". - To add more subjects to a set, call extend_image_set with the id of any batch in it, as often as the set needs. - If a "Rate limit exceeded" error is returned, wait the suggested number of seconds before retrying. Do not retry immediately. - Showing the result: as soon as the call returns, show the user every delivered image before you put them to use, then keep working without waiting for a reply (unless the user asked to review first), so they can ask for a change before anything is built on them. Show each image exactly as delivered, as its own file: the files are the product, and their exact size, margin and background are what the user asked for. Never present a new image made from them, even to check them; it hides all three. Files on disk or a link in your context are not delivery. Where you can open files on the user's machine, open the extracted index.html with the host's file opener; it shows each image at its delivered size with its dimensions. Where you can attach files to your reply, attach each delivered file as it is (attaching is not inspecting). Otherwise give the online viewer link and the zip link. Show every image unless the user asked for only some, and say in one line what you left out; if you can show none, say the set was made but you could not show it, and give the viewer link.ConnectorAPI key
- Statically audit an MCP tool surface from a public HTTPS URL or tools/list snapshot. Returns deterministic scores and findings without invoking any target tool or making LLM calls. When the user asks to check another installed MCP server, read that server's complete tool definitions from client context and pass them as snapshot (MCP `name` or Cursor-style `tool` both work; do not use file paths or $ref). If those definitions are unavailable, ask the user for its public endpoint or tools/list JSON instead of inventing an audit.ConnectorNo auth
- Depreciate an asset by straight line or diminishing value, with a year by year schedule. Use for: 'how is depreciation calculated', 'straight line vs diminishing value', 'depreciation schedule', 'book value after 3 years', 'work out depreciation on equipment'. THE TWO METHODS DIVERGE SHARPLY ON THE SAME ASSET. Straight line spreads the deduction evenly across the useful life. Diminishing value applies the rate to the REDUCING book value, so it front-loads the deduction heavily and never reaches zero on its own. Which is better depends entirely on when the owner wants the deduction, so the year by year schedule is the useful part of this answer rather than the first year figure. Show the schedule. additional_costs are capitalised into the cost base, which is where delivery and installation belong. salvage_value acts as a floor under both methods, and the last year of a straight line life takes only what is left, so rounding cannot push the book value below it. ONE IMPORTANT LIMIT, AND IT MATTERS. This is the ARITHMETIC of the two methods on a rate you supply. It is NOT a lookup of Inland Revenue's prescribed rates, which vary by asset class and are published in the IRD depreciation rate finder. If someone needs to know the right RATE for a particular asset, send them to that finder. DO NOT INVENT A RATE to feed this tool and present the output as their tax position. Args: initial_cost: Purchase price of the asset. method: straight_line or diminishing_value. additional_costs: Costs capitalised in, such as delivery or installation. salvage_value: Expected residual value. A floor under both methods. useful_life_years: Years of life. Required for straight line. annual_rate_pct: Rate per year. Required for diminishing value. years_to_schedule: Years to show. Defaults to the life, or ten. Returns: The cost base, the depreciable amount, the first year charge, the total charged, the closing book value and the full year by year schedule.ConnectorNo auth
- Embed a text string into a 768-dim Gemini Flash vector. Use for HyDE-style retrieval: (1) write a hypothetical short paper that would perfectly answer the user's query, (2) embed it with task_type='RETRIEVAL_DOCUMENT' (default — matches the corpus embedding side), (3) pass the resulting embedding back through search-style tools to find real papers nearest to the hypothetical. task_type='RETRIEVAL_QUERY' matches the query side and is useful for direct user-query embedding without HyDE. Pro-only — requires an SF_API_KEY on a Pro account; anonymous and free callers get a 403 pro_required. Cost: ~$0.0001/call; rate-limited at 30/minute per API key.ConnectorNo auth
- Generate an empty background plate for one short shot. Both reference inputs steer what the plate itself looks like — layout, prop identity, materials and lighting — and are unrelated to upload_shot_continuity_reference, which guides clip generation instead. Use reference_image_asset_ids for images already in this project (find them with get_asset_library) and reference_images_base64 to send bytes you produced locally, for example a frame lifted from an earlier scene so a prop matches what is already on screen. Uploaded bytes are used for this one generation and are not kept as project assets. The location's own reference images are always included on top of both. Spends credits. Returns a job id — poll it with get_job.ConnectorNo auth
- PROJECT-SCOPED: this call acts only on the explicit project_id and returns the project identity with its result. Change how existing captions LOOK without touching their text or timing. Pass only the fields to change: 'make the captions premium' -> {"style":{"motion_look":"editorial"}}, 'calmer/cleaner' -> {"style":{"motion_look":"clean"}}, 'more hype/viral' -> {"style":{"motion_look":"pop"}}, 'big hero words' -> {"style":{"motion_look":"lockup"}} (motion looks are browser-drawn; see add_captions for them and for the preset menu: clean/documentary/broadcast/podcast/beast/karaoke/spotlight/elegant/stacked/.../classic; naming a new preset without motion_look renders that preset, motion_look:null returns to the stored preset), 'make it red' -> {"style":{"color":"#FF0000"}}, 'center the captions' -> {"style":{"position":"middle"}}, 'bigger / more dynamic captions' -> {"style":{"size":"xl","dynamic":true}} (dynamic = legacy karaoke without a preset; presets animate on their own). highlight_color changes the accent of emphasized/spoken words; uppercase forces casing; emphasis_words (top-level arg, with a preset) replaces the emphasized keyword list. An explicit position or anchor_y locks captions there for the whole video and removes any old shot-aware placement track — use this when the user says captions must stop moving. For fine size control that the s|m|l|xl buckets can't hit pass size_scale (0.5-3.0; 1.5 = 50% bigger). Outline, shadow, backing panel, tracking and text alignment are independently editable. For transcript captions, single_line:true overrides any preset stack and guarantees one rendered row per state. Works for from_transcript and manual captions; errors helpfully if no captions exist yet.ConnectorOAuth