Skip to main content
Glama
510,021 tools. Updated 2026-09-03 16:33

"Tools or methods to generate anime-style images" matching MCP tools:

  • ONLY for video montage/stitching/export workflows. Use when the user explicitly asks to create a montage, stitch clips, make a reel, export a video sequence, make video clips from images, or combine images/videos into one final video. Never use this for a photoshoot, lookbook, product shoot, collection shoot, outfit shoot, garment shoot, or image-generation request; those must use request_user_context followed by propose_brief/update_brief. Do not call this merely because selected context contains images, generations, garments, or models. A photoshoot may later feed a montage, but the photoshoot itself must be proposed as a BriefProposal first. PROPOSES the montage for user review — user can edit clips, generate missing videos, then export. Supports: existing videos with optional trim (`target_duration` or `start_time`/`end_time`), images that need video generation (specify video_model + a bespoke per-image motion prompt, and optionally `target_duration` or `duration`), per-clip speed/mute, global aspect ratio. If the user asks for clips to be e.g. '3 seconds each', set `target_duration: 3` on every item, including image items. For image items, avoid generic repeated prompts: tailor each prompt to the specific image and any requested zoom, movement, energy, or camera direction. If motion is not specified, inspect the image first with view_image and then write a fitting motion prompt from the image content before proposing. The user reviews and confirms in the UI. Export is free (0 credits); video generation clips cost credits per their model.
    Connector
  • List the images the user ALREADY has in their Orivox media gallery -- uploads made on the editor's media page or through Orivox before -- as hosted URLs ready to use DIRECTLY in <img src> or apply_dom_ops set_attr. Check here FIRST whenever the user mentions "my images", their gallery, or pictures they already uploaded, before asking them to upload or send anything. role="logo" lists the logo folder, "content" the general image folder, "all" (default) both, newest first. Each entry carries url, file_name, width/height (null when unknown), and modified_at. Returns the newest `limit` images (default 24, max 500); truncated=true with a larger total means older images exist -- re-call with a bigger limit only when the user actually needs them. Use the urls exactly as returned -- never rewrite them through the a12 grammar. Read-only; changes nothing.
    Connector
  • Return the EXACT images the user chose on their upload link. Pass the token_id that request_image_upload_link returned. Call this after the user says they uploaded or picked their images: it returns files[], each with a hosted url and a source ("upload", "gallery", or "shared"), so you place PRECISELY the images they selected instead of guessing from the whole gallery. An empty files list means they have not chosen anything yet -- ask them to open the link and add images, or wait and check again. Read-only; changes nothing.
    Connector
  • 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.
    Connector
  • Generate game-art images from a text prompt alone, selecting an image_type (e.g. sprite) and optionally art_style, perspective, and aspect_ratio. The job result is an array of image results, each with a url; request n (1-8) to control how many variations come back. Because it generates purely from text it takes no source image, so there is no upload size limit to trip. Credits are charged only on success, scaled to the number of images produced. Use createImage to make new images from scratch; use generateWithStyle to match a reference image's art style, editImage to modify an existing image, and removeBackground to cut out a subject. Pass an optional request_id to tag the results so you can retrieve them later via `GET /assets/images/results`. Requires an API key (user scope). Returns 202 with a job id immediately; poll `getApiJob` (pass `wait: 30`) until status is succeeded, then read its `result` field, which is exactly the response documented for this operation. Each account may have up to 50 generations queued or running at once; beyond that submissions return 429 (PENDING_JOBS_LIMIT) - wait for jobs to finish. Credits: This endpoint consumes 0.5 credits per result.
    Connector
  • Generate new images that match the visual style of a reference image: supply a style_image (URL or base64) plus a text prompt describing what to create and an image_type (defaults to sprite). The job result is an array of image results, each with a url; request n (1-4) to control the number of variations. The style_image is uploaded and validated, and an image larger than 15MB is rejected with HTTP 400. Credits are charged only on success, scaled to the number of images produced. Use this instead of createImage when style consistency with an existing asset matters; use editImage to alter the content of a specific image rather than borrow its style, and removeBackground to isolate a subject. Pass an optional request_id to tag the results so you can retrieve them later via `GET /assets/images/results`. Requires an API key (user scope). Returns 202 with a job id immediately; poll `getApiJob` (pass `wait: 30`) until status is succeeded, then read its `result` field, which is exactly the response documented for this operation. Each account may have up to 50 generations queued or running at once; beyond that submissions return 429 (PENDING_JOBS_LIMIT) - wait for jobs to finish. Credits: This endpoint consumes 0.5 credits per result.
    Connector

Matching MCP Servers

  • A
    license
    A
    quality
    C
    maintenance
    Enables 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.
    8
    MIT
  • A
    license
    Not graded
    quality
    D
    maintenance
    Provides 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

Matching MCP Connectors

  • Diagrams, badges, charts and QR codes as plain image URLs you can paste into Markdown.

  • Still losing time to small decisions? Spin or Flip brings randomization into Claude so you can offload mental load to chance instantly.

  • Modify an existing image according to text instructions: supply a source image (URL or base64) and a prompt describing the changes (e.g. "add clouds", "warmer color scheme"), with an optional reference_image for extra style or content guidance. The job result is an array of image results, each with a url; request n (1-4) to control the number of edited variations. Provided images are uploaded and validated, and any image larger than 15MB is rejected with HTTP 400. Credits are charged only on success, scaled to the number of images produced. Use editImage to transform a specific existing image; use createImage to generate from text alone, generateWithStyle to borrow a reference's art style, and removeBackground for the dedicated background-removal case. Pass an optional request_id to tag the results so you can retrieve them later via `GET /assets/images/results`. Requires an API key (user scope). Returns 202 with a job id immediately; poll `getApiJob` (pass `wait: 30`) until status is succeeded, then read its `result` field, which is exactly the response documented for this operation. Each account may have up to 50 generations queued or running at once; beyond that submissions return 429 (PENDING_JOBS_LIMIT) - wait for jobs to finish. Credits: This endpoint consumes 0.5 credits per result.
    Connector
  • Generate a video from 1-5 reference images and a text prompt (references-to-video). Unlike createVideo, which animates a single source image, this composes a new scene that borrows characters, objects, and style from the reference images. Each image can be a URL or base64. The job result is the video URL and its actual duration in seconds. Choose the output shape with `aspect_ratio` ("default" lets the model decide). The chosen `model` and `duration` must be compatible (incompatible combinations return HTTP 400). Credits are charged only on success, based on the produced duration and never more than the duration you requested. Pass an optional `request_id` to tag the result so you can locate it later via `GET /assets/videos/results`. Related tools: `createVideo` for image-to-video, `editVideo` to modify a generated video. Requires an API key (user scope). Returns 202 with a job id immediately; poll `getApiJob` (pass `wait: 30`) until status is succeeded, then read its `result` field, which is exactly the response documented for this operation. Each account may have up to 50 generations queued or running at once; beyond that submissions return 429 (PENDING_JOBS_LIMIT) - wait for jobs to finish. Credits: cost varies by model and duration (credits/sec): Eagle 1.5/s, Eagle with Audio 2/s, Forge Pixel 2/s (min 4); see this endpoint's full pricing table in the API docs.
    Connector
  • 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`.
    Connector
  • Render one of a style's two template images — a REAL step of style setup, not an optional extra: a style isn't finished until both its character and environment templates are rendered (the app shows them on the style card). Asset reference images render against them (characters → character template; environments and objects → environment template), and segment renders fall back on them when a shot has no asset reference — so finish BOTH before generate_asset_reference. Run once per template_type ("character" | "environment") for every new style; skip types the style already has (get_style's `templates`). A template already exists is a hard stop here — the call refuses unless replace=True, because overwriting one silently re-anchors every future render. Async — await_jobs(style_id=...), then get_style.
    Connector
  • 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.
    Connector
  • Generate a cohesive SET of custom images with TRANSPARENT backgrounds, each one a separate isolated subject sharing one visual style (icons, logos, sprites, UI assets that need to drop onto any backdrop). The style parameter says how everything is drawn; the subjects parameter says what to draw. 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), so an existing set can be extended in its original style across many calls. Returns a zip download URL. Each call costs 1 credit. When a solid colored background fits the user's use case, generate_image_set (1 credit per call) is the faster choice. Run generation calls sequentially, never in parallel - only one generation runs at a time per API key. Output formats: PNG (lossless) or WebP. JPG is not supported because it has no alpha channel. Size and quality considerations match generate_image_set: leaving width and height unset delivers native resolution (best quality, varies between generations); fixing them resamples to that box, and fixing one axis lets the other hug each subject. 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). - Image 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 a single composed scene (a figure set within an environment), use generate_illustration 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 18 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. Image 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_image_set. - Icons: the subject count sets how large a batch's icons can later be exported with export_icons, crisp at every density: about a 136px base size with 13 to 18 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". - If a "Rate limit exceeded" error is returned, wait the suggested number of seconds before retrying. Do not retry immediately.
    Connector
  • Kitsu anime and manga database — the highest-ranked anime on Kitsu, ordered by popularityRank (default) or ratingRank, up to 20 entries. Each entry returns the Kitsu anime id, titles, both rank positions, average rating, episode count and synopsis. Answers which anime are most popular or highest rated according to Kitsu.
    Connector
  • Generate one or more finished images from a template (get a template_id from recommend_template or browse_templates) plus a description. Use this after the user has selected or explicitly supplied a template_id; otherwise call `recommend_template` first so the visual gallery can collect the selection. Works for all categories (Instagram, logo, app-store, Visual Novel backgrounds/sprites, CG illustrations). Pass variants for multi-image output (expressions, time-of-day, etc.). Pass session_id to refine a prior result. Pass context_ids to ground a new image on prior designs (character consistency for VN CG scenes). Set model to 'minimax-h3-image-balanced' or 'minimax-h3-image-quality' (or use the 'h3 balanced'/'h3 quality' aliases) to render through the MiniMax H3 image service; context_ids are forwarded as ordered H3 reference images.
    Connector
  • Edit a previously generated video with a text prompt and optional reference images (video-to-video). Pass the video `url` you received from `createVideo`, `createVideoFromReferences`, or an earlier edit - it must be a video you generated within the last 7 days; arbitrary external videos are not accepted. Optionally add up to 5 reference `images` (URL or base64) to guide the edit. The job result is the new video URL and its actual duration in seconds. Credits are charged only on success, based on the produced duration and never more than the duration you requested. Pass an optional `request_id` to tag the result so you can locate it later via `GET /assets/videos/results`. Related tools: `createVideo` to generate the source clip, `createVideoFromReferences` for reference-driven generation. Requires an API key (user scope). Returns 202 with a job id immediately; poll `getApiJob` (pass `wait: 30`) until status is succeeded, then read its `result` field, which is exactly the response documented for this operation. Each account may have up to 50 generations queued or running at once; beyond that submissions return 429 (PENDING_JOBS_LIMIT) - wait for jobs to finish. Credits: cost varies by model and duration (credits/sec): Eagle 2/s, Forge Pixel 2/s (min 4); see this endpoint's full pricing table in the API docs.
    Connector
  • Rig a 3D model: generate a skeleton and skin weights for an existing GLB so it can be animated. Accepts a URL or base64-encoded GLB in `model`. The job result is a downloadable `model_url` for the rigged GLB. rig_type selects the skeleton prior - general (default, any asset), humanoid (anime-style characters), game (classic game-character rigs), or the pinned humanoid templates for two-armed, two-legged characters: humanoid_template (standard 22-joint skeleton with named joints, required for animating from the preset library) and humanoid_template_hands (52 joints, five fingers per hand). joint_naming relabels the identified joints to a convention - smpl (default), mixamo, humanik, unreal, godot, rigify, or vroid - without changing the skeleton. Credits are charged only on success. Rigging is non-destructive to geometry but replaces any prior skeleton, so animations made against an old rig no longer apply. Requires an API key (user scope). Returns 202 with a job id immediately; poll `getApiJob` (pass `wait: 30`) until status is succeeded, then read its `result` field, which is exactly the response documented for this operation. Each account may have up to 50 generations queued or running at once; beyond that submissions return 429 (PENDING_JOBS_LIMIT) - wait for jobs to finish. Credits: This endpoint consumes 1 credits per call.
    Connector
  • Use this when the user wants to preview or generate a chart. Validate each generated or changed config once before rendering, saving, or updating. Do not revalidate unchanged input; a successful render preflights it for later saves or updates. Errors block; warnings require explicit user approval for the exact config. Suggestions do not block. Replace the complete config with suggestedConfig and revalidate before repair. Stores a 1 hour preview and returns public image URLs. Authenticated previews use the account's render allowance, and each image produced from a returned URL is another render. Anonymous previews are complimentary; their images use the anonymous image allowance.
    Connector
  • Generate images using the public /v2/inferences endpoint. For the highest quality prefer RD Pro styles (rd_pro__*); they support reference_images for character/style consistency, and most go as small as 12x12 px (check list_available_styles for each style's limits) — a small target size is never a reason to switch to a cheaper model family. Style ids are opaque strings with no uniform format (some RD Fast styles appear as "default:rd_flux"); take them verbatim from the catalog and never infer capabilities from an id's prefix. For animation styles prefer start_inference_job + get_inference_job instead — animations are long-running, and a failed animation is worth one retry with identical parameters (failures auto-refund). Field-tested workflow rules: N distinct items = N individually usable images (separate calls or num_images=N), never one sheet/grid image unless a sheet IS the deliverable. Variants of ONE image (seasons, day/night, palettes) = generate the base once, then derive each variant with the image_edit tool ("... keep the exact same composition") — independent generations of the "same" scene come out unrelated. Converting an existing image INTO pixel art is rd_pro__pixelate with input_image; reference_images-based generation re-imagines rather than converts. To animate an image you already have, use rd_advanced_animation__* with input_image (fixed-format rd_animation__* styles generate their own subject from the prompt instead). Use `input_image` for the main source image, `reference_images` for extra per-inference guidance, and `style_reference_images` only on create_user_style/update_user_style. The response excludes raw base64 image payloads to keep MCP outputs compact.
    Connector
  • Generate images using the public /v2/inferences endpoint. For the highest quality prefer RD Pro styles (rd_pro__*); they support reference_images for character/style consistency, and most go as small as 12x12 px (check list_available_styles for each style's limits) — a small target size is never a reason to switch to a cheaper model family. Style ids are opaque strings with no uniform format (some RD Fast styles appear as "default:rd_flux"); take them verbatim from the catalog and never infer capabilities from an id's prefix. For animation styles prefer start_inference_job + get_inference_job instead — animations are long-running, and a failed animation is worth one retry with identical parameters (failures auto-refund). Field-tested workflow rules: N distinct items = N individually usable images (separate calls or num_images=N), never one sheet/grid image unless a sheet IS the deliverable. Variants of ONE image (seasons, day/night, palettes) = generate the base once, then derive each variant with the image_edit tool ("... keep the exact same composition") — independent generations of the "same" scene come out unrelated. Converting an existing image INTO pixel art is rd_pro__pixelate with input_image; reference_images-based generation re-imagines rather than converts. To animate an image you already have, use rd_advanced_animation__* with input_image (fixed-format rd_animation__* styles generate their own subject from the prompt instead). Use `input_image` for the main source image, `reference_images` for extra per-inference guidance, and `style_reference_images` only on create_user_style/update_user_style. The response excludes raw base64 image payloads to keep MCP outputs compact.
    Connector
  • Find productions by name (accent- and case-insensitive substring match), best match first then by how widely Wikipedia covers them. Filter by kind: film, tv, game or anime. Use it when you are unsure of a title before calling where_was_it_filmed. Games and anime are placed by where they are SET, never where they were filmed; the `relation` field on every result says which.
    Connector