"A Model Context Protocol (MCP) server for Leonardo.ai image generation" matching MCP tools:

• read_docsA

  clipkitA

  Return a canonical Clipkit doc as text. topic "card" = the ~8KB compact authoring card — the recommended context for authoring; "pattern-data-viz" / "pattern-cinematic-ui" / "pattern-ui-screencast" = ~4-5KB archetype pattern cards (proven idioms: count-ups and bar rows; product hero shots with camera rigs; faked app UI with typing/cursor/clicks) — load ONE alongside the card when the brief matches its archetype; "agents" = the full authoring guide (fetch only when the card doesn't cover a need); "protocol" = the formal field spec; "brand" = brand reference. (Same docs offered as MCP resources, exposed as a tool so you can read them directly — resources are not always model-readable.)

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• propose_montageA

  UwearA

  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.

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• aetherwave_generate_imageA

  AetherWave StudioA

  Generates one or more images from a text prompt (T2I) or a text prompt + reference image(s) (I2I). Submits the job, polls until terminal, and returns the final image URLs. Default model is 'grok-imagine-t2i' (fast, 6 images per generation, 5 credits). Use list_image_models to see the full lineup with pricing. For I2I, pass `referenceImages` as an array of public image URLs and pick a model with I2I support (e.g. 'grok-imagine-i2i', 'wan-2.5-spicy-i2i'). ## Model selection guide (when the user does not specify a model) Default: `grok-imagine-t2i` (5 cr, 6 outputs per call, fast, general purpose). **Strong recommendation: when a single high-quality output is what's wanted** (most agent / one-shot workflows), prefer `gpt-image-2-t2i` (9 cr @ 1K / higher @ 2K, single deterministic image, best general quality across realism, illustration, typography, and composition; supports up to 2K resolution and most aspect ratios including auto). This is the front-runner for serious creative output where you don't need to pick from 6 variations. Pick a different model when the prompt has these signals: - "single best result" / "one image" / production / no time to pick from variations -> `gpt-image-2-t2i` (9 cr, 1 output, top general quality) - "photoreal" / "photo of" / "realistic" -> `gpt-image-2-t2i` (9 cr, best general realism) or `imagen-4` (12 cr, very high quality) or `z-image-turbo` (3 cr, fastest) - "highest quality" / "premium" / no budget -> `gpt-image-2-t2i` at 2K, or `grok-imagine-quality-t2i` (16 cr @ 1K, 22 cr @ 2K), or `imagen-4-ultra` - Text inside the image (signs, posters, typography) -> `ideogram-v3-t2i` (best in class) or `gpt-image-2-t2i` (also strong) - Artistic / painterly / stylized -> `midjourney-t2i` - Album art / cover art -> `gpt-image-2-t2i` for one strong image; `grok-imagine-t2i` for 6 variations to choose from; `seedream-v4-t2i` if 4K wanted - Logo or design with embedded text -> `ideogram-v3-t2i` - NSFW / adult / explicit -> `wan-2.5-spicy-t2i` (auto-tags creation as 18+; routes to adult gallery) - Cheapest possible / quick test -> `z-image-turbo` (3 cr) - Multiple variations to compare -> keep `grok-imagine-t2i` (6 outputs default) or use `numImages` on a multi-output model For I2I (reference image provided): prefer the dedicated `aetherwave_edit_image` tool for "change something in this image" intent. Use `aetherwave_generate_image` with I2I models only when you specifically want style transfer (`midjourney-i2i`), premium quality (`grok-imagine-quality-i2i`), or adult content (`wan-2.5-spicy-i2i`). Always pass an explicit `aspectRatio` (e.g. "1:1" for square album art, "16:9" for video thumbnails, "9:16" for shorts/reels). Some upstream providers reject submissions with no aspect ratio. Ask the user only when: - The prompt contradicts itself (e.g., "highest quality but cheapest") - The user requested "the best model" with no context, surface 2-3 options with tradeoffs - A single generation would cost more than 20 credits and the user has not confirmed

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• siteborne_get_service_healthA

  SITEBORNE Utility NetworkA

  Report MCP server readiness and production-enable status for each SITEBORNE service. Use when: an agent must check protocol availability, tool count, or whether a service is currently production-enabled before selecting a paid tool. Do not use when: a quote is needed (use siteborne_get_quote) or company, web, document, or agent-output evidence work is required (use the corresponding SITEBORNE service tool). Behavior: read-only and credential-independent; it does not perform paid evidence work, create quotes, verify payment, call providers, write service state, create Workflows, or settle. Economics: free of charge; it never issues a payment challenge. Failure: it depends on no provider, so it does not fail because a provider is unavailable. Returns: the server and protocol versions plus truthful local, production, and external-publication status for all four evidence services.

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• generate_imageA

  GoodeyeA

  Generate one or more images from a text prompt, billed to the caller's credits. Requires authentication. Anonymous image generation is available only via the REST API (``POST /v1/image-generators/{id}/runs``); the MCP transport always authenticates. Resolution order for the generator (highest priority first): 1. A deployed ``generator`` ref (``uuid@version`` or bare UUID): pins the deployed version config. 2. The ``model`` control path (authenticated one-off, ephemeral). Not usable from published templates. 3. A tier ``generator`` ref (``system:<tier>``): resolves to the tier's current best model (auto-upgrade). Available tiers: ``system:image-standard`` (default), ``system:image-premium``, ``system:image-edit`` (image-to-image, requires ``reference_image_url``). 4. Default: ``system:image-standard`` when no generator or model is given. ``generator`` and ``model`` are mutually exclusive. For ``image_to_image`` generators, ``reference_image_url`` is required and must be a public HTTP or HTTPS URL. For ``text_to_image`` generators, providing ``reference_image_url`` is rejected. Billing: spend is deducted from the caller's monthly credit balance. ``BudgetExhausted`` (402) and ``AccountSuspended`` (403) propagate if the balance is zero or the account is suspended. ``visibility`` sets the access level of the hosted copy of each image: ``public`` (default) returns a link that opens in any browser; ``private`` returns a link only you can open and forward to people you choose, while the plain URL stays locked. Returns: ``{run_id, model_tier_or_model, image_url, image_urls, width, height, num_images, cost_usd, duration_ms, status, created_at, error_code, error_message, hosted_images}``. ``hosted_images`` carries the durable Goodeye-hosted copy of each image with its ``url`` (the browser-viewable link) and ``visibility``. The prompt is never stored; only its hash is persisted on the run row.

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• create3DModelA

  Ludo AI Game AssetsA

  Convert a single source image into a textured 3D model (image-to-3D). The job result is a downloadable GLB model_url plus an array of snapshot image URLs rendered from different angles (handy for previews). Accepts optional mesh controls: target_num_faces (max triangle count, 1000-200000, default 50000), texture_size (1024 or 2048, default 2048), and texture_type ("pbr", "simple", or "none", default "pbr"). Credits are held when the job is accepted and refunded if it fails or is cancelled. Pass an optional request_id to tag the result so you can locate it later via listGenerations (type 3d). Async generation job: returns `{id, status}` - poll `getApiJob` (job and credit contract: see the server instructions). Credits: This endpoint consumes 3 credits per call.

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• recommend_mcpA

  SaSame MCP Observatory + Gold Rush TownB

  Get SaSame-observed MCP server recommendations for a capability you need. SaSame, operated by SASAME S.R.L., continuously observes and measures the Model Context Protocol ecosystem and publishes verifiable evidence and history; the MCP Factory is internal machinery and an optional product surface behind it; measurement only, not endorsement. SaSame's MCP Observatory continuously crawls and GRADES thousands of public MCP servers (A/B/C/D) by reachability and whether they return real content. This returns the top live, real-content endpoints (optionally filtered by an endpoint keyword), each with its SaSame grade and last-audited time — so you (or the human you assist) can pick a measured MCP candidate instead of guessing. Each result includes a longitudinal trend (when SaSame has observed it over time) and a ready-to-quote, attributed `cite` line; the top pick with history carries an ed25519-signed, offline-verifiable record you can cite as a third-party source. Free, read-only, no signup. If you operate one of these servers, claim it (claim_start). If you can't find a fit and need an MCP/agent BUILT, call engage_sasame. Pass a referral handle from `refer` as engage_sasame(ref=...) to attribute the introduction.

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• generate_video_from_imageA

  openFlowA

  Generate a video that starts from a given image (i2v — image-to-video). start_image_base64: raw base64-encoded image bytes, no data: URI prefix. Mutually exclusive with reference-image (r2v) generation. For "make content that looks like this real photo of a person" specifically, prefer generate_video_with_reference (r2v) over this — see its docstring for why. i2v is better suited to "continue visually from this exact frame" (e.g. chaining clips), not identity preservation across a whole new scene. beats: how many DISTINCT action phases the prompt describes — "ears flick back", "tail lashes", "strikes the ball", "ball flies off" are four. Give it and the server picks the clip length for you (about two phases per second) and says in the reply what it picked and why. Measured 31.08.2026: the model STRETCHES whatever you describe over whatever length you ask for, so one phase in four seconds comes out as slow motion, and the same text at six seconds instead of four loses 15% of its movement. Adjectives are not phases. Leave beats out and the length you passed is used unchanged. project_id: put the clip into a project made with create_project instead of the account's default one.

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• deploy_image_generatorA

  GoodeyeA

  Deploy a reusable image generator that skills reference to produce images from a chosen model: creates it or appends a version. An image generator is a named, versioned configuration that routes image generation calls to a specific model. Generators are private and owner-scoped. Skills reference them by UUID or ``uuid@version``. You cannot deploy a new generator whose ``name`` matches an active platform ``scope=system`` generator (those are tier-level configs that are run-only and not listed or fetched). Versioning: the first deploy with a given ``name`` creates the generator at version 1. Re-deploying the same ``name`` appends a new version and requires ``expected_version_token`` from the latest known version (returned by deploy/list/get). A new generator must omit the token; an existing one without a token returns Conflict. Deploy-time validation: the ``model`` is checked against the pricing layer. A model that does not resolve to a known image endpoint with an authoritative price is rejected before any row is written. Returns: ``{generator_id, name, description, current_version, version, version_token, status, scope, provider, model, generation_contract, config_hash, created_at}``. Persist ``version_token`` for the next re-deploy.

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• agent_briefA

  Colour MemoryA

  Generate a complete colour direction package for another AI agent or image generation model. Fetches a historically grounded archive palette from the concept, then produces: an agent brief (colour direction in prose), colour tokens with hex values and roles, a model-specific image generation prompt, a negative prompt, and lighting notes. Supports midjourney, flux, dalle, stable_diffusion. Example: task='luxury hotel bedroom', concept='Ottoman winter luxury', model='midjourney'. Use this to make Colour Memory the colour layer for other AI systems. Archive-grounded retrieval is evidence-filtered: entries with claim_role='reject' (no primary source and no period connection), stub entries, blank-source entries, and entries below minimum_claim_strength are never selected. If fewer than palette_size colours pass these filters, the call returns an honest incomplete result (ok:false, error_code:INSUFFICIENT_EVIDENCE_ELIGIBLE_PALETTE) rather than padding the palette with rejected or weak evidence. Negative constraints (from 'avoid' or negation phrases in concept like 'must never', 'without', 'not') are also applied to retrieval, not just flagged afterward -- a brief that says a wedding must never feel funereal will not surface mourning-themed colours in the first place. locked_palette calls skip evidence filtering entirely since the caller is supplying colours directly, not requesting archive evidence.

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• aetherwave_balanceA

  AetherWave StudioA

  Returns the current AetherWave credit balance for the API key. Use this BEFORE a generation to confirm sufficient credits, especially for video which can cost 30-300+ credits depending on model/duration/resolution.

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• aetherwave_list_image_modelsA

  AetherWave StudioA

  Returns every image-generation model AetherWave supports, with its credit cost, default aspect ratio, supported inputs (T2I vs I2I), and any model-specific options. Call this before generate_image when you don't know the right model ID. The model key (e.g. 'grok-imagine-t2i') is what you pass as `model` to generate_image.

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• aetherwave_list_video_modelsA

  AetherWave StudioA

  Returns every video-generation model AetherWave supports (Grok Imagine, Wan 2.7, Hailuo 02, Seedance Pro/Lite, Kling 2.6 with audio, VEO 3.1, Happy Horse, etc.) with per-second credit cost, supported durations, resolutions, aspect ratios, and whether the model needs an input image (I2V). Call this before generate_video when you don't know the right model ID.

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• generate_cover_imageA

  Misar.Blog MCP ServerA

  Generate an image from a text prompt with AI, upload it to the Misar.Blog CDN, and return its public URL for use as cover_image_url when publishing. Use it when no artwork exists yet; use upload_image for a file the user already has. Each call generates a NEW image and costs generation credits against the account's plan — it is not idempotent, so re-running to 'try again' bills again. Generation takes noticeably longer than other tools. Requires an API key. The resulting URL is public and cannot be deleted through this server. Results vary between runs for the same prompt.

  ConnectorNo auth

• agent_briefA

  Colour MemoryA

  Generate a complete colour direction package for another AI agent or image generation model. Fetches a historically grounded archive palette from the concept, then produces: an agent brief (colour direction in prose), colour tokens with hex values and roles, a model-specific image generation prompt, a negative prompt, and lighting notes. Supports midjourney, flux, dalle, stable_diffusion. Example: task='luxury hotel bedroom', concept='Ottoman winter luxury', model='midjourney'. Use this to make Colour Memory the colour layer for other AI systems. Archive-grounded retrieval is evidence-filtered: entries with claim_role='reject' (no primary source and no period connection), stub entries, blank-source entries, and entries below minimum_claim_strength are never selected. If fewer than palette_size colours pass these filters, the call returns an honest incomplete result (ok:false, error_code:INSUFFICIENT_EVIDENCE_ELIGIBLE_PALETTE) rather than padding the palette with rejected or weak evidence. Negative constraints (from 'avoid' or negation phrases in concept like 'must never', 'without', 'not') are also applied to retrieval, not just flagged afterward -- a brief that says a wedding must never feel funereal will not surface mourning-themed colours in the first place. locked_palette calls skip evidence filtering entirely since the caller is supplying colours directly, not requesting archive evidence.

  ConnectorNo auth

• agent_briefA

  Colour MemoryA

  Generate a complete colour direction package for another AI agent or image generation model. Fetches a historically grounded archive palette from the concept, then produces: an agent brief (colour direction in prose), colour tokens with hex values and roles, a model-specific image generation prompt, a negative prompt, and lighting notes. Supports midjourney, flux, dalle, stable_diffusion. Example: task='luxury hotel bedroom', concept='Ottoman winter luxury', model='midjourney'. Use this to make Colour Memory the colour layer for other AI systems. Archive-grounded retrieval is evidence-filtered: entries with claim_role='reject' (no primary source and no period connection), stub entries, blank-source entries, and entries below minimum_claim_strength are never selected. If fewer than palette_size colours pass these filters, the call returns an honest incomplete result (ok:false, error_code:INSUFFICIENT_EVIDENCE_ELIGIBLE_PALETTE) rather than padding the palette with rejected or weak evidence. Negative constraints (from 'avoid' or negation phrases in concept like 'must never', 'without', 'not') are also applied to retrieval, not just flagged afterward -- a brief that says a wedding must never feel funereal will not surface mourning-themed colours in the first place. locked_palette calls skip evidence filtering entirely since the caller is supplying colours directly, not requesting archive evidence.

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• mcp_searchA

  Find MCP - Agentage MCP DirectoryA

  Search the agentage MCP directory - a public catalog of Model Context Protocol servers crawled from the official registry - for servers matching a keyword, optionally narrowed by type, category, language, or license. Use this FIRST whenever the user wants to discover, find, compare, or pick an MCP server ("is there an MCP for X", "which MCP servers do Y"). Results are ranked by text relevance to the query first, then by popularity, so the best match is on top. Returns a page of lean cards (slug, title, description, category, transport, match_score - text relevance the ranking is based on, details_url). To read one server's full packages, tools, and install command, call mcp_get with the slug from a result; open a card's details_url for the human detail page. Valid category, language, and license values come from the mcp_categories tool, not from guesswork - call it before filtering and pass its labels verbatim, or the call is rejected. Read-only - never installs or runs anything.

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• check_urlA

  WatchpupA

  Check any public URL RIGHT NOW: is it up, HTTP status, response time in ms. With kind:"mcp" it instead performs a real JSON-RPC initialize handshake against a streamable-HTTP MCP server endpoint and reports the server’s self-declared name/version/protocol — useful to tell "the MCP server is down" from "my client is misconfigured". Works without an API key (rate limit 30/hour per IP). For continuous monitoring with alerts, use create_monitor.

  ConnectorNo auth

• generate_cover_imageA

  Misar.Blog MCP ServerA

  Generate an image from a text prompt with AI, upload it to the Misar.Blog CDN, and return its public URL for use as cover_image_url when publishing. Use it when no artwork exists yet; use upload_image for a file the user already has. Each call generates a NEW image and costs generation credits against the account's plan — it is not idempotent, so re-running to 'try again' bills again. Generation takes noticeably longer than other tools. Requires an API key. The resulting URL is public and cannot be deleted through this server. Results vary between runs for the same prompt.

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• list_tasksA

  mcpA

  List the account's past generation tasks, newest first, with pagination. Filter by status (processing/completed/failed), model id, category (video/image/audio/text/llm), provider, or a created_after/created_before time window. Use it to find an earlier generation's task_id (for get_task, extend_video, or the veo/grok upscalers) or to review recent spend. Within one filter, comma-separated values are OR'd; different filters are AND'd.

  ConnectorNo auth

• apply_backdropA

  UwearA

  Remove or replace an image background through Uwear's canonical free backdrop generation path. Provide exactly one foreground source: generation_result_id, clothing_item_asset_id, or a public HTTPS image_url. Use backdrop_mode transparent, solid with color_hex, or image with background_image_url. Returns generation_id; poll get_generation_status, then fetch get_generation_results.

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