MCP-сервер, позволяющий Claude Code генерировать изображения и короткие видео через API Google Gemini (Imagen 4 для статичных изображений, Veo 3.1 для видео).
Я создавал веб-сайты с помощью Claude Code и постоянно сталкивался с одной и той же проблемой. Claude разрабатывал отличный макет, а затем говорил: «добавьте сюда свое главное изображение». Мне приходилось генерировать его где-то еще, возвращаться, подключать его, вносить правки, повторять. Переключение контекста было главной проблемой. Поэтому я создал этот MCP-сервер, чтобы замкнуть цикл. Claude пишет промпт, сервер обращается к Veo или Imagen, файл попадает в директорию проекта, и Claude ссылается на него в коде, который он уже пишет. Одна сессия, никаких лишних действий.
Я выкладываю это в открытый доступ, потому что другие люди решают ту же проблему, и никому не нужно создавать это дважды.
Related MCP server: Gemini Image & Video Generation MCP
Claude Code генерирует визуальные элементы с помощью visualgen-mcp и встраивает их на страницу.
https://github.com/user-attachments/assets/34d25755-cd4c-47b0-a0cb-056b9ed8b783
Шесть инструментов, доступных через stdio:
submit_video(prompt, model, aspect_ratio, resolution, negative_prompt?, image_path?) — отправка задания в Veo 3.1. Мгновенно возвращает job_id.
check_video(job_id) — опрос состояния задания. Когда видео готово, сервер скачивает его и возвращает путь.
list_videos() — список MP4-файлов в выходной директории, начиная с самых новых.
generate_image(prompt, model, aspect_ratio, negative_prompt?) — синхронная генерация изображений (обычно менее 10 секунд). Возвращает путь к PNG.
list_images() — список PNG-файлов в выходной директории.
get_pricing() — возвращает текущие тарифы Gemini, чтобы Claude мог предупредить вас перед дорогостоящими операциями.
Тарифы Gemini API по состоянию на 22.04.2026. Проверяйте актуальность на официальной странице цен перед использованием в продакшене.
Модель | Стоимость |
Veo 3.1 Standard (720p / 1080p) | $0.40 / сек |
Veo 3.1 Standard (4k) | $0.60 / сек |
Veo 3.1 Fast (720p) | $0.10 / сек |
Veo 3.1 Fast (1080p) | $0.12 / сек |
Veo 3.1 Fast (4k) | $0.30 / сек |
Veo 3.1 Lite (720p) | $0.05 / сек |
Veo 3.1 Lite (1080p) | $0.08 / сек |
Imagen 4 Fast | $0.02 / изобр. |
Imagen 4 Standard | $0.04 / изобр. |
Imagen 4 Ultra | $0.06 / изобр. |
Gemini 2.5 Flash Image (Nano Banana) | $0.039 / изобр. |
Для конечных пользователей:
uvx visualgen-mcpДля контрибьюторов (из исходного кода):
git clone https://github.com/1RaghavM/visualgen-mcp.git
cd visualgen-mcp
uv sync
uv run python -m visualgen_mcpЗапустите интерактивную настройку один раз. Она сохранит ваш профиль в ~/.config/visualgen-mcp/config.toml (chmod 600) и при необходимости настроит .mcp.json в текущей директории проекта.
uvx visualgen-mcp initВас попросят ввести:
Gemini API key — получите его на aistudio.google.com/apikey. Для Veo требуется платный тариф; Imagen и Nano Banana работают на бесплатном уровне, но с более строгими лимитами.
Output directory — место, куда сохраняются сгенерированные PNG и MP4. По умолчанию ~/visualgen-output. Сервер создает её, если она не существует, а ответы инструментов возвращают абсолютные пути, чтобы Claude мог ссылаться на файлы в коде.
Default video tier, image model, and aspect ratios — используются, когда Claude вызывает инструмент, не указывая эти параметры.
Запускайте visualgen-mcp init в любое время, чтобы обновить профиль. Переопределения для конкретных проектов все еще работают: GEMINI_API_KEY или OUTPUT_DIR, установленные в локальном файле .env или в блоке env файла .mcp.json, имеют приоритет над профилем.
Либо запустите visualgen-mcp init внутри вашего проекта (он предложит записать это за вас), либо добавьте это в .mcp.json в корне вашего проекта:
{
"mcpServers": {
"visualgen": {
"command": "uvx",
"args": ["visualgen-mcp"]
}
}
}Сервер считывает ваш API-ключ и настройки по умолчанию из ~/.config/visualgen-mcp/config.toml. Если вы хотите переопределить их для конкретного проекта, установите GEMINI_API_KEY или OUTPUT_DIR в блоке env файла .mcp.json — переменные окружения имеют приоритет над профилем.
Введите /mcp в Claude Code, чтобы убедиться, что сервер подключен. Вы должны увидеть visualgen в списке с шестью инструментами.
/visualgenСервер поставляется с навыком для Claude Code, который учит Claude, когда использовать каждый инструмент, как структурировать промпты для типичных задач веб-дизайна и когда подтверждать расходы с вами. Если вы запускали visualgen-mcp init, он уже предложил установить его. В противном случае:
cd your-project
cp -r /path/to/visualgen-mcp/.claude/skills/visualgen ./.claude/skills/visualgenПосле установки команда /visualgen hero video, modern SaaS landing, calm mood запускает весь конвейер за один раз — Claude выбирает шаблон, составляет промпт, отправляет задание и встраивает полученный файл в ваш код. Вы также можете просто запрашивать визуальные элементы на естественном языке — навык автоматически загружается, когда Claude собирается вызвать инструмент visualgen-mcp.
Навык учитывает стоимость: Veo standard и любой вызов преобразования изображения в видео всегда требуют вашего подтверждения перед расходом средств. Все остальное — вся генерация изображений плюс преобразование текста в видео через Veo lite/fast — выполняется без лишних вопросов.
Claude Desktop использует ту же схему. Добавьте запись в раздел mcpServers в ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) или %APPDATA%\Claude\claude_desktop_config.json (Windows). Любой MCP-клиент с поддержкой stdio, поддерживающий вызовы инструментов, будет работать. Здесь нет HTTP или уровня аутентификации — сервер предназначен для локального использования.
Реальный запуск в проекте лендинга. Пропущенные строки отмечены ....
> Build me a hero video loop for the top of the page. Something that suggests "city at night, calm, slow."
I'll generate that with the visualgen server.
submit_video(
prompt="A slow drone shot over a city skyline at night, warm window lights,
occasional headlights tracing streets below, calm mood, cinematic",
model="fast",
aspect_ratio="16:9",
resolution="1080p"
)
→ { "job_id": "f3c1...", "status": "submitted", "model": "veo-3.1-fast-generate-preview",
"estimated_seconds": 60 }
I'll check back in a minute.
check_video("f3c1...")
→ { "status": "pending", "elapsed_seconds": 25 }
... wait ...
check_video("f3c1...")
→ { "status": "complete",
"path": "/Users/.../landing/generated/veo-fast-20260422T191203Z.mp4",
"duration_seconds": 8.0 }
Writing it into the hero section:
Edit: src/components/Hero.tsx
<video
autoPlay
muted
loop
playsInline
className="absolute inset-0 h-full w-full object-cover"
src="/generated/veo-fast-20260422T191203Z.mp4"
/>Один промпт, одна генерация, файл в проекте, код обновлен.
submit_videoПараметр | Тип | По умолчанию | Примечания |
|
| (обязательно) | Описание видео. |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| Текст, описывающий, чего следует избегать. |
|
|
| Абсолютный путь к начальному кадру PNG/JPEG/WebP. |
Возвращает {"job_id": str, "status": "submitted", "model": str, "estimated_seconds": int}.
check_videoПараметр | Тип | По умолчанию |
|
| (обязательно) |
Возвращает одно из:
{"status": "pending", "elapsed_seconds": int}
{"status": "complete", "path": str, "duration_seconds": float}
{"status": "failed", "error": str}
list_videosБез параметров. Возвращает list[{"path": str, "size_bytes": int, "created_at": str}], начиная с самых новых.
generate_imageПараметр | Тип | По умолчанию | Примечания |
|
| (обязательно) | Описание изображения. |
|
|
|
|
|
|
|
|
|
|
| Игнорируется Nano Banana. |
Возвращает {"path": str, "model_used": str}.
list_imagesБез параметров. Возвращает list[{"path": str, "size_bytes": int, "created_at": str}], начиная с самых новых.
get_pricingБез параметров. Возвращает словарь текущих тарифов для каждой модели, дату last_updated и ссылку на источник. Зашито в код — сервер не вызывает API для этого.
Клипы Veo ограничены 8 секундами. Этот сервер всегда запрашивает 8 секунд.
Для Veo нет бесплатного уровня. Каждая успешная генерация оплачивается.
Сгенерированные видео удаляются с серверов Google через 48 часов после генерации. Скачивайте их (через check_video) в течение этого окна.
Хранилище заданий находится в оперативной памяти. Если вы перезапустите сервер, ожидающие задания будут потеряны. См. TODO в src/visualgen_mcp/jobs.py для пути миграции на SQLite.
Некоторые промпты отклоняются контент-фильтрами Google. Вы получите {"status": "failed", ...}, а не сбой программы.
Nano Banana (gemini-2.5-flash-image) быстрее и дешевле, чем Imagen 4, но не принимает negative_prompt. Используйте Imagen 4, когда он вам нужен.
list_videos и list_images видят только файлы в OUTPUT_DIR. Они не выполняют рекурсивный поиск и не переходят в другие директории.
См. CONTRIBUTING.md.
MIT. См. LICENSE.
Check a video generation job. Downloads the file when ready.
Returns one of three shapes:
{"status": "pending", "elapsed_seconds": int}
{"status": "complete", "path": str, "duration_seconds": float}
{"status": "failed", "error": str}
Safe to call repeatedly. Once a job is complete or failed, subsequent calls return the cached terminal state without re-polling.
Args: job_id: The id returned by submit_video.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure and excels. It reveals key traits: polling behavior ('Downloads the file when ready'), idempotency ('Safe to call repeatedly'), caching ('returns the cached terminal state'), and the three possible return shapes with their semantics. This goes well beyond basic parameter documentation.
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?
The description is perfectly structured and concise. It front-loads the core purpose, then details return shapes, behavioral notes, and parameters in logical order. Every sentence earns its place with essential information, and there is no wasted verbiage.
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?
Given the tool's complexity (stateful job monitoring), lack of annotations, and presence of an output schema (which covers return values), the description is complete. It explains the tool's role in a workflow, behavioral guarantees, parameter semantics, and output interpretation, leaving no significant gaps for an AI agent.
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 schema description coverage is 0%, so the description must compensate. It adds crucial semantic context for the single parameter: 'job_id: The id returned by submit_video' clarifies the parameter's origin and relationship to another tool. However, it doesn't specify format constraints (e.g., UUID, string length) that might be in an unannotated schema.
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 clearly states the tool's purpose with specific verbs ('Check', 'Downloads') and resource ('video generation job'), distinguishing it from siblings like 'submit_video' (which creates jobs) and 'list_videos' (which lists existing videos). It explicitly describes the monitoring and file retrieval functionality.
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 provides explicit guidance on when to use this tool: 'Check a video generation job' implies it's for monitoring jobs created by 'submit_video'. It also specifies 'Safe to call repeatedly' and explains terminal state behavior, giving clear operational context without misleading exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate an image synchronously. Blocks until the file is on disk.
Typical latency is under 10 seconds. The PNG is written to the configured OUTPUT_DIR and the absolute path is returned.
Args: prompt: Text description of the image to generate. model: "nano-banana" (Gemini 2.5 Flash Image, the default — cheapest and fastest) or "imagen" (Imagen 4, higher quality, higher cost). aspect_ratio: "1:1", "16:9", "9:16", "4:3", or "3:4". negative_prompt: Optional text describing what to avoid. Ignored by Nano Banana; used by Imagen 4 when supplied.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | ||
| model | No | nano-banana | |
| aspect_ratio | No | 16:9 | |
| negative_prompt | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: synchronous operation ('Blocks until the file is on disk'), typical latency ('under 10 seconds'), file handling ('PNG is written to OUTPUT_DIR'), and return value ('absolute path is returned'). It also notes model-specific behavior differences (e.g., negative_prompt ignored by Nano Banana). However, it lacks details on error handling or rate limits.
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?
The description is appropriately sized and front-loaded: the first sentence states the core purpose and key behavioral trait (synchronous blocking). Subsequent sentences efficiently cover latency, output details, and parameter explanations in a structured format with bullet-like clarity. Every sentence adds value without redundancy.
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?
Given the complexity (image generation with multiple parameters), no annotations, and the presence of an output schema (which handles return value documentation), the description is complete enough. It covers purpose, behavior, parameters, and output location, leaving the output schema to detail the return structure. No critical gaps remain for agent understanding.
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?
Schema description coverage is 0%, so the description must fully compensate. It provides comprehensive parameter semantics: 'prompt' as the text description, 'model' with two options and cost/quality trade-offs, 'aspect_ratio' with five specific options, and 'negative_prompt' with optional usage and model-specific behavior. This adds significant meaning beyond the bare schema, fully documenting all four parameters.
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 clearly states the tool's purpose: 'Generate an image synchronously' with specific details about the output format (PNG) and location (OUTPUT_DIR). It distinguishes from sibling tools like 'list_images' (which retrieves existing images) and 'submit_video' (which handles video generation), making the verb+resource combination specific and differentiated.
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 provides clear context for when to use this tool: for synchronous image generation with latency under 10 seconds. It distinguishes from alternatives by specifying the output format (PNG) and location (OUTPUT_DIR), but does not explicitly state when NOT to use it or compare it to other image-related tools beyond the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return current published Gemini API pricing for the models this server uses.
Rates are per-image for images and per-second for video. Pricing is
hardcoded from the official docs (see the source field) and may drift;
always verify before expensive operations.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it returns 'current published' pricing, notes that rates vary by media type (images vs. video), mentions the data source ('hardcoded from the official docs'), and warns about potential drift and the need for verification. This covers most behavioral aspects for a read-only pricing tool.
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?
The description is front-loaded with the core purpose in the first sentence, followed by supporting details. Every sentence earns its place by clarifying pricing units, data source, and reliability warnings. It is appropriately sized with no wasted words.
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?
Given the tool's simplicity (0 parameters, no annotations, but with an output schema), the description is complete. It covers purpose, usage context, behavioral details like data source and limitations, and output semantics. The presence of an output schema means the description doesn't need to explain return values, so this is sufficient.
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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description adds value by explaining the semantics of the returned pricing data (e.g., 'per-image for images and per-second for video'), which goes beyond the schema. A baseline of 4 is appropriate for zero-parameter tools with high schema coverage.
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 clearly states the tool's purpose: 'Return current published Gemini API pricing for the models this server uses.' It specifies the verb ('Return'), resource ('Gemini API pricing'), and scope ('models this server uses'), distinguishing it from sibling tools like generate_image or submit_video that perform different operations.
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 provides clear context for when to use this tool: to get pricing information for API operations. It implies usage before 'expensive operations' but does not explicitly state when not to use it or name alternatives among sibling tools, which would be needed for a score of 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List every PNG file in the configured OUTPUT_DIR, newest first.
Each entry has absolute path, size in bytes, and creation timestamp.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it lists files from a specific directory (OUTPUT_DIR), sorts them (newest first), and describes the return format (absolute path, size, creation timestamp). It doesn't mention permissions, rate limits, or error conditions, but covers the core behavior adequately.
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?
The description is perfectly concise with two sentences that each earn their place. The first sentence states the core action and scope, while the second provides essential output details. No wasted words, well-structured and front-loaded.
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?
Given the tool's simplicity (0 parameters, no annotations, but has output schema), the description is complete enough. It explains what the tool does, how results are sorted, and what data each entry contains. The output schema will handle return value details, so the description doesn't need to explain return values further.
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 0 parameters with 100% schema description coverage, so the baseline is 4. The description doesn't need to add parameter information, and it appropriately focuses on the tool's behavior rather than parameters.
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 clearly states the tool's purpose with specific verbs ('List every PNG file') and resources ('in the configured OUTPUT_DIR'), including sorting order ('newest first'). It distinguishes from sibling tools like 'list_videos' by specifying PNG files rather than videos.
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 provides clear context for when to use this tool (listing PNG files in OUTPUT_DIR), but doesn't explicitly state when not to use it or name alternatives. It implies usage for PNG files specifically, which helps differentiate from 'list_videos' for video files.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List every MP4 file in the configured OUTPUT_DIR, newest first.
Each entry has absolute path, size in bytes, and creation timestamp.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it lists files 'newest first', specifies the exact fields in each entry (path, size, timestamp), and mentions the configured OUTPUT_DIR as the source. It lacks details on permissions or error handling, but covers core behavior adequately.
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?
The description is perfectly concise and front-loaded: the first sentence states the core purpose and key details (MP4 files, OUTPUT_DIR, ordering), and the second sentence adds essential output structure. Every sentence earns its place with zero waste.
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?
Given the tool's low complexity (0 parameters, simple list operation), no annotations, and the presence of an output schema (which handles return values), the description is complete enough. It covers purpose, behavior, and output fields, leaving no significant gaps for this context.
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 0 parameters with 100% schema description coverage, so the baseline is 4. The description adds no parameter information, which is appropriate since there are no parameters to document, maintaining clarity without redundancy.
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 clearly states the specific action ('List every MP4 file') and resource ('in the configured OUTPUT_DIR'), with explicit scope details. It distinguishes from sibling tools like 'list_images' by specifying MP4 files only, not images.
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 provides clear context about when to use this tool (to list MP4 files in OUTPUT_DIR), but does not explicitly state when not to use it or name alternatives like 'list_images' for non-MP4 files or 'check_video' for individual video inspection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Submit a Veo 3.1 video generation job. Returns immediately with a job_id.
Videos typically take 30-120 seconds. Call check_video(job_id) to poll for completion. The final MP4 is written to the configured OUTPUT_DIR and its absolute path is returned by check_video once ready.
Args: prompt: Text description of the video to generate. model: "lite" (cheapest), "fast" (default, good balance), or "standard" (highest quality, most expensive). See get_pricing for rates. aspect_ratio: "16:9" (landscape) or "9:16" (portrait). resolution: "720p", "1080p", or "4k". "4k" is rejected for model="lite". negative_prompt: Optional text describing what to avoid. image_path: Optional absolute path to a PNG/JPEG/WebP file to use as the starting frame for image-to-video generation.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | ||
| model | No | fast | |
| aspect_ratio | No | 16:9 | |
| resolution | No | 720p | |
| negative_prompt | No | ||
| image_path | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the asynchronous nature (returns immediately with job_id), typical processing time (30-120 seconds), polling mechanism (call check_video), output location (written to OUTPUT_DIR), and format (MP4). This covers execution flow, timing, and output handling beyond basic functionality.
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?
The description is appropriately sized and well-structured: it starts with the core purpose and immediate return, then explains the asynchronous workflow, and finally details each parameter in a clear Args section. Every sentence adds value without redundancy, making it efficient and easy to parse.
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?
Given the complexity of an asynchronous video generation tool with 6 parameters, no annotations, and an output schema (implied by 'Returns immediately with a job_id'), the description is complete. It covers the tool's purpose, behavioral workflow, parameter semantics, and integration with sibling tools, providing all necessary context for an agent to use 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?
Schema description coverage is 0%, so the description must fully compensate. It provides detailed semantic explanations for all 6 parameters: purpose of prompt, model options with cost/quality trade-offs, aspect ratio meanings, resolution options with constraints (4k rejected for lite), optional negative_prompt usage, and image_path for image-to-video. This adds substantial meaning beyond the bare schema.
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 clearly states the specific action ('Submit a Veo 3.1 video generation job') and resource ('video'), distinguishing it from siblings like check_video (polling), generate_image (image creation), and list_videos (listing). It uses precise verbs and identifies the exact resource being created.
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 provides clear context for when to use this tool (to start a video generation job) and explicitly mentions check_video as the follow-up for polling completion. However, it doesn't explicitly state when NOT to use it or compare it to alternatives like generate_image for image generation, leaving some sibling differentiation implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0check_videogenerate_imageget_pricinglist_imageslist_videossubmit_videoScored across 6 tools
Each tool has a clearly distinct purpose with no ambiguity. check_video monitors video jobs, generate_image creates images, get_pricing provides cost information, list_images and list_videos enumerate outputs, and submit_video initiates video generation. The separation between image and video operations is particularly clear.
All tools follow a consistent verb_noun pattern with snake_case throughout. The naming is predictable and readable: check_video, generate_image, get_pricing, list_images, list_videos, and submit_video. This consistency makes the tool set easy to understand at a glance.
Six tools is well-scoped for a visual generation server. This provides complete coverage for both image and video workflows without being overwhelming. Each tool earns its place by addressing a specific need in the generation, monitoring, listing, or pricing aspects of the service.
The tool set covers the core visual generation domain comprehensively with create, check, and list operations for both images and videos, plus pricing information. The only minor gap is the lack of a tool to delete or manage generated files, but agents can work around this using system file operations.
MCP server for Google Veo AI video generation
Use AI models for chat, image, and video generation from Claude Code and other MCP hosts.
MCP server for Qwen Image 3 AI image generation
MCP server for Grok Imagine AI video generation
