imaginate-mcp
imaginate-mcp
MCP-сервер, который создаёт и редактирует изображения с помощью OpenAI GPT Image и Google Gemini (Nano) (Nano Banana). Он работает через stdio, сохраняет каждое изображаение на диске и возвращает путь к файлу, чтобы ваш ассистент мог продолжить работу с резолютатом.
Что вы получите
Шесть инструментов, розспелённых по провайдерам:
Инструмент | Назначение |
| scriptная генерация ихображений по тексту с помощью моделей GPT Image |
| редактирование одного изображения, инпейнтинг по маске или работать с нескольких референцев |
| идентивноп-иденетные процедуры, их сильные стороны и лимиты |
| генерация изображений по тексту с помощью моделей Nano Banana, с по выборной опции привязки к Google Search |
| редактирование, перенос стиля, семантический инпейнт или композицию из нескольких из обращений |
| иденфикторы моделей, лимиты на гллю.референсы и уровень resolution |
Только те инструменты регистрируются, каким соответствуют настроеные keyчи. Если задать только OPENAI_API_KEY, ваш ассист увидит три "рументы, и ни one из них не сделать ошибку из-за отсутстствующего ключа Google. Это и был о главный причинno, by которой инструменты разедёнyt по провайдером, а не сдела ней как one струмент с аргументом provider.
Related MCP server: MCP OpenAI Image Generation Server
Требования
Node.js 20 или новее
API-ключ OpenAI, Gemin key or bothAPI both
Модели GPT Image требуют верификации orgанизации в OpenAI. Пока вы не прошли её, OpenAI откlonяt запрос, and server сообщит вам об этом.
Подключение
Запустите опубликованный пакт с npx. Можно not клonировать..
npx -y @pinkpixel/imaginate-mcpДобавьте сервер в конфигурацyou. For Claude Desktop, отредактируйте claude_desktop_config.json. For Claude Code, use .mcp.json in a project or user's settings.
{
"mcpServers": {
"imaginate": {
"command": "npx",
"args": ["-y", "@pinkpixel/imaginate-mcp"],
"env": {
"OPENAI_API_KEY": "sk-...",
"GEMINI_API_KEY": "...",
"IMAGINATE_OUTPUT_DIR": "~/Pictures/imaginate"
}
}
}
}После изменения конфигурации перзапустите клиент. If you have not yet появились image-инструменты, вызовите imaginate_setup_help. This инструмент есть только then, когda no provider is found, and he lists them in variables that you need to set.
Запуск из исходников
Клонируйте and соберите, if you want to work with server locally.
git clone https://github.com/pinkpixel-dev/imaginate-mcp.git
cd imaginate-mcp
npm install
npm run build
node dist/index.jsTo подкл. MCP-клиент к этаму сборку, use "command": "node" and in args assign the absolute path to dist/index.js.
Конфигурация
Все переменные считываются once at startup, so if you change some, restart the client.
Variable | Required | Default | What it does |
| Требуется один из ключей | there | Registers |
| Требуется one of the keys |
| Reg. с tool |
| нет |
| Кatalog where images are saved. А |
| нет |
| Model used if no model is set in call |
| нет |
| Model used if no model is set in call |
| нет | OpenAI default | Point to an OpenAI-compatible proxy |
Любой вызов can overridden output directory with output_dir, and file name via filename.
As working the files
Изображения попадают в выходной каталог. The server never overwrites anything. cat.png that already exists — becomes cat-1.png, then cat-2.png.
Имена по умолчанию look lie openin: tha is prefixed. Thas prefix, slug вашего запроса, and time stamp. If you want something specific, pass filename.
Sources for edit must be local files. Pass absolute paths. The tools are not downloads, so if the file is online, download it first. It only read and never changes.
Use
Server connected — basically you talk like ordinary to the assistant. A couple of things useful.
Choose provider
Both providers are good — in different.
Gemini is stronger on text inside images, world knowledge, and infographic work. It can rely on live Google Search results. It also returns an interaction ID — you can improve the picture without uploading again.
GPT Image is well meets detailed layout instructions and gives good control over size, quality, background. Need transparent background? Use it, but then you need gpt-image-1.5 or an older model, because gpt-image-2 removed it.
Iteration in Gemini image
Each turn result includes an interaction ID. Pass it forward as previous_interaction_id in next gemini_edit_image and not upload image again:
gemini_generate_imagewith a prompt. Return with the result includes ID.gemini_edit_imagewithprevious_interaction_idand the prompt «make it horizontal».
This is a cheap alternative to re-upload and more stable image between rounds.
Edit and composition
Both *_edit_image can handle several tasks through same interface. Pass one image path to edit it; pass several to assemble the new scene.
For mask inpainting the provides differ. OpenAI expects a real mask PNG with alpha channel, sent as mask. Gemini does it semantically: simply say «change only the sky and leave everything else» and skip the mask file.
Reference limit: 14 for Lite, 10 for Nano Banana 2, and 6 for Pro. In doubt, call gemini_list_image_models.
Development
npm run build # compile to dist/
npm run watch # compile on change
npm run typecheck # types only, no output
npm test # compile tests and run themTests use a built-in Node runner. They cover naming and saving, Gemini response parse, and error matching. No API call, so they run without keys.
The layout:
src/
index.ts entry point, conditional tool registration
config.ts environment parsing
lib/ file handling, errors, result formatting, model catalog
providers/openai/ OpenAI client wrapper and tool definitions
providers/google/ Gemini client wrapper and tool definitions
tests/Ограничения
Исходные images должны быть локальными — nothing on URL.
Streaming output не реализована. A call is returned, when done.
Gemini not reliable fulfilling amount, so button set one image per request. OpenAI the tools have
nand it works.OpenAI может использовать до fifteen minutes per complicated request. That's API difference, server not guilty.
Every image from Gemini contains invisible SynthID.
Model IDs and prices move fast both. List tools show what version knows; actual access may differ.
Лицензия
Apache 2.0. Смо. LICENSE.
Made with 💖 — Pink Pixel.
Available Tools
1 toolimaginate_setup_helpImaginate setup helpARead-onlyIdempotent
Explain why this server currently has no image generation tools and how to enable them.
This tool only exists when no image provider API key is configured. Call it if you expected image generation tools and cannot find any.
Args: none.
Returns: The environment variables to set and where to set them.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds that the tool returns the environment variables to set and where to set them, plus the precondition for its existence. This gives the agent concrete expectations beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Four short sentences each carry necessary information: purpose, existence condition, usage trigger, and return value. The structure is front-loaded with the core purpose and contains no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Even without an output schema, the description fully covers what the tool does, when to invoke it, and what it returns. For a zero-parameter informational tool, an agent has everything needed to call it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and schema coverage is 100%, so there is nothing meaningful to add. The description redundantly states 'Args: none,' which is harmless but adds no semantic value. The zero-parameter baseline of 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific purpose: explaining why the server lacks image generation tools and how to enable them. The conditional existence context makes the tool's role unmistakable even without sibling tools to compare against.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says the tool only exists when no image provider API key is configured, and instructs agents to call it if expected image generation tools cannot be found. This is a clear, actionable when-to-use rule.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
1 tool update
v1.0.0- First observed
imaginate_setup_help
TDQS
With only one tool available, there is zero ambiguity in tool selection. The lone tool's purpose is clearly stated and distinct from any hypothetical generation tools it replaces.
The single tool name 'imaginate_setup_help' follows a consistent pattern of server name plus action, and since there is only one tool, there are no naming inconsistencies to evaluate.
A server named 'imaginate' with only a setup help tool is severely under-provisioned for its apparent purpose. Image generation typically requires multiple operational tools, so having only one explanatory tool is an extreme mismatch.
The server provides no actual image generation capabilities at all—the only tool explains how to configure an API key. This is a severely incomplete surface for the stated domain, leaving agents unable to perform any core task.
Maintenance
Related MCP Connectors
Generate images with any major model — one API key, one prepaid balance, one MCP.
OCR, transcription, file extraction, and image generation for AI agents via MCP.
Edit images over MCP with object removal, background removal, and guided generative edits.
Generate AI images and videos from any compatible MCP client.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceProvides tools for generating and editing images using OpenAI's gpt-image-1 model via an MCP interface, enabling AI assistants to create and modify images based on text prompts.15Apache 2.0
- AlicenseNot gradedqualityNot gradedmaintenanceEnables AI assistants to generate and edit images through OpenAI's DALL-E models via MCP tools. Supports text-to-image generation and image-to-image editing with configurable parameters for size, quality, and style.-
- AlicenseAqualityAmaintenanceGenerates and edits images via Gemini, Grok, and GPT-image providers for MCP clients like Claude Code that lack native image generation.319MIT
- AlicenseAqualityCmaintenanceEnables LLMs to generate images via MCP by calling AI models from providers like SiliconFlow, OpenAI, or custom APIs, with tools for image generation and model listing.2MIT
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