AminA Character Studio

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\\n

AminARECORDS

Character Studio

● ChatGPT connected

\\n \\n

\\n

Character references

Identity only · up to 10 files

0/10

\\n ＋ Add character referencesSingle photos or reference collages\\n

\\n \\n \\n

\\n

Destinations

Scene, pose, outfit and style

0/10

\\n ＋ Add destination imagesEach image becomes one separate output\\n

\\n

Generation queue

Continuous · no confirmation stops

\\n

No collage. One destination equals one standalone result. Failed items retry independently.

\\n \\n

\\n

Results ready

Select only the images you want to upscale

\\n

0 selected

\\n

\\n External fallback ↗\\n

\\n

\\n\\n\\n\";\n\nconst handler = createMcpHandler(() => {\n const server = new McpServer({ name: \"amina-character-studio\", version: \"1.3.0\" });\n\n const resourceMeta = {\n ui: {\n prefersBorder: true,\n domain: \"https://amina-character-studio-plugin.val.run\",\n csp: { connectDomains: [], resourceDomains: [] },\n },\n \"openai/widgetDescription\": \"Interactive AminA Character Studio for identity-locked character transfer and selective upscaling.\",\n \"openai/widgetPrefersBorder\": true,\n \"openai/widgetDomain\": \"https://amina-character-studio-plugin.val.run\",\n \"openai/widgetCSP\": {\n connect_domains: [],\n resource_domains: [],\n redirect_domains: [STUDIO_URL],\n },\n };\n\n registerAppResource(server, \"amina-character-studio-widget\", TEMPLATE_URI, {}, async () => ({\n contents: [{\n uri: TEMPLATE_URI,\n mimeType: RESOURCE_MIME_TYPE,\n text: widgetHtml,\n _meta: resourceMeta,\n }],\n }));\n\n server.registerResource(\"amina-character-studio-widget-skybridge\", LEGACY_TEMPLATE_URI, {}, async () => ({\n contents: [{\n uri: LEGACY_TEMPLATE_URI,\n mimeType: \"text/html+skybridge\",\n text: widgetHtml,\n _meta: resourceMeta,\n }],\n }));\n\n const openStudioDescriptor = {\n title: \"Open AminA Character Studio\",\n description: \"Render the interactive AminA Character Studio directly inside the ChatGPT conversation. This is the primary UI-rendering tool.\",\n inputSchema: z.object({}),\n outputSchema: z.object({\n mode: z.literal(\"embedded\"),\n studioUrl: z.string().url(),\n ready: z.boolean(),\n }),\n annotations: { readOnlyHint: true, destructiveHint: false, openWorldHint: false, idempotentHint: true },\n _meta: {\n ui: { resourceUri: TEMPLATE_URI, visibility: [\"model\", \"app\"] },\n \"openai/outputTemplate\": TEMPLATE_URI,\n \"openai/widgetAccessible\": true,\n \"openai/toolInvocation/invoking\": \"Opening Character Studio…\",\n \"openai/toolInvocation/invoked\": \"Character Studio ready\",\n },\n };\n\n const openStudioHandler = () => ({\n structuredContent: { mode: \"embedded\" as const, studioUrl: STUDIO_URL, ready: true },\n content: [{ type: \"text\" as const, text: \"AminA Character Studio is mounted as an interactive inline component. The external URL is fallback only.\" }],\n });\n\n registerAppTool(server, \"open_character_studio\", openStudioDescriptor, openStudioHandler);\n registerAppTool(server, \"open_character_studio_ui\", {\n ...openStudioDescriptor,\n title: \"Render AminA Character Studio UI\",\n description: \"Explicitly render the interactive AminA Character Studio component inline. Use this if a cached client treated open_character_studio as text-only.\",\n }, openStudioHandler);\n\n server.registerTool(\"prepare_character_batch\", {\n description: \"Prepare an uninterrupted identity-locked character transfer batch using images attached in the current ChatGPT conversation.\",\n inputSchema: z.object({\n referenceCount: z.number().int().min(1).max(10),\n destinationCount: z.number().int().min(1).max(10),\n subject: z.enum([\"woman\", \"man\", \"both\", \"main-subject\"]).default(\"main-subject\"),\n additionalInstruction: z.string().max(2000).optional(),\n }),\n }, ({ referenceCount, destinationCount, subject, additionalInstruction }) => ({\n content: [{ type: \"text\", text: [\n \"AMINA CHARACTER BATCH READY.\",\n \"References: \" + referenceCount + \"; destinations: \" + destinationCount + \"; subject mapping: \" + subject + \".\",\n \"Use the attached reference files only to lock identity. A reference file may itself be a collage.\",\n \"Use each destination for pose, framing, camera angle, gaze, expression, outfit and coverage, lighting, environment, occlusion, body presentation, and visual medium.\",\n \"Generate exactly \" + destinationCount + \" independent full-frame outputs: one destination equals one separate result.\",\n \"Never create a collage, grid, montage, contact sheet, split screen, or storyboard.\",\n \"Run one continuous batch without confirmation stops. Retry only a failed item and continue the remaining items.\",\n \"Match the destination medium. For photographs use maximum-quality photorealistic natural texture; for illustrations render the locked identity in that same style.\",\n \"Keep anatomy and posture plausible and natural.\",\n additionalInstruction ? \"Additional instruction: \" + additionalInstruction : \"Additional instruction: none.\",\n \"Use the native image generation/editing capability available in this ChatGPT session; do not request an API key for this workflow.\",\n \"Visual workspace: \" + STUDIO_URL,\n ].join(\"\\n\") }],\n }));\n\n server.registerTool(\"prepare_upscale_batch\", {\n description: \"Prepare a separate 2x or 4x upscale batch for only the completed results selected by the user.\",\n inputSchema: z.object({\n selectedCount: z.number().int().min(1).max(10),\n factor: z.enum([\"2x\", \"4x\"]),\n }),\n }, ({ selectedCount, factor }) => ({\n content: [{ type: \"text\", text: [\n \"UPSCALE BATCH READY: \" + selectedCount + \" selected result(s), \" + factor + \".\",\n \"Upscale only the selected completed results as one uninterrupted batch.\",\n \"Return each enhanced image as a separate file.\",\n \"Preserve identity, face, composition, skin texture, anatomy, styling, and destination constraints.\",\n \"Enhance resolution and natural detail only; do not reshape, recombine, or regenerate the scene.\",\n ].join(\"\\n\") }],\n }));\n\n return server;\n});\n\nexport default handler.fetch;","deleted","label",[19],{"_36":83,"_13":84,"_60":85,"_62":-5,"_63":64,"_65":86,"_67":68,"_38":87,"_17":88,"_70":24,"_72":89,"_74":51,"_75":-5,"_80":90,"_18":91},{"_36":59,"_13":19,"_60":61,"_62":-5,"_63":64,"_65":66,"_67":68,"_38":79,"_17":32,"_70":71,"_72":73,"_74":51,"_75":14,"_80":81,"_18":82},["D",1789475979314],"children",[],[19],"71540e14-6f1c-474f-a3f4-0b8707d6b4f6","README.md","01a0a4ce-0e3e-7033-86bd-6ee0de657ed0",1,["D",1789471362559],0,"# MCP server\n\n_In video form:\n[How to build an MCP server in 1 minute](https://youtu.be/sO6-Wl5fy7g?si=oc7s9bVIVFHxHJh2)_\n\nA minimal MCP server with one example tool (that gets the current time in a\ngiven timezone).\n\nRemix this val to create your own MCP server on Val\nTown—[**_just like the MCP Core Maintainer team!_**](https://blog.val.town/mcp-core-maintainers)\n\nThis MCP server (<50 LOC in [`main.ts`](./main.ts)) is live on\n[mcp-time.val.run](https://mcp-time.val.run) if you'd like to test it\n([playground.ai.cloudflare.com](https://playground.ai.cloudflare.com) is a great\nplace to test an MCP server quickly). 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