jimeng-ai-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| JIMENG_OUTPUT_DIR | No | 生成文件保存目录,默认 ~/jimeng-output | |
| JIMENG_ACCESS_KEY_ID | Yes | 火山引擎 Access Key ID | |
| JIMENG_SECRET_ACCESS_KEY | Yes | 火山引擎 Secret Access Key |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generate_imageA | 文生图:根据文字描述生成图片。
|
| image_to_imageA | 图生图(智能参考):参考输入图片,结合文字描述编辑或生成新图片。 使用即梦图生图 3.0 模型(jimeng_i2i_v30)。
|
| inpaint_imageA | 局部重绘(Inpainting):在图片的指定区域(mask)内根据提示词重新生成内容。 使用即梦交互编辑模型(jimeng_image2image_dream_inpaint)。
|
| upscale_imageA | 智能超清:将图片放大并增强细节至 4K 或 8K 分辨率。 使用即梦智能超清模型(jimeng_i2i_seed3_tilesr_cvtob)。
|
| generate_videoA | 文生视频:根据文字描述生成视频(约需 1~5 分钟)。
|
| image_to_videoA | 图生视频:将静态图片转换为动态视频(约需 1~5 分钟)。
recamera 模式可用运镜模板(在 prompt 中指定 template_id): hitchcock_dolly_in / hitchcock_dolly_out / robo_arm / dynamic_orbit / central_orbit / crane_push / quick_pull_back / counterclockwise_swivel / clockwise_swivel / handheld / rapid_push_pull |
| imitate_motionA | 动作模仿 2.0:提取参考视频中的动作/表情/口型,迁移到目标人物图片上。 使用模型:jimeng_dreamactor_m20_gen_video
输出视频约需 3~5 分钟(10 秒视频约需 180 秒)。 |
| generate_digital_humanA | 数字人生成(OmniHuman 1.5):驱动人物图片口型和动作,配合音频生成说话视频。 使用模型:jimeng_realman_avatar_picture_omni_v15
注意:此接口使用 CVSubmitTask/CVGetResult(与普通接口不同)。 |
| translate_videoA | 视频翻译 2.0:将视频中的语音翻译并口型同步为目标语言。 使用模型:video_translate_v2_cvtob
支持的语言代码: zh(中文)en(英语)ja(日语)ko(韩语)fr(法语)de(德语) es(西班牙语)pt(葡萄牙语)ru(俄语)ar(阿拉伯语)it(意大利语) hi(印地语)id(印尼语)nl(荷兰语)tr(土耳其语)pl(波兰语)等 |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 9 tools
Each tool targets a distinct media generation or editing task: text-to-image, image-to-image, inpainting, upscaling, text-to-video, image-to-video, motion imitation, digital human creation, and video translation. There is no meaningful overlap between these operations.
All tool names use a consistent lowercase_snake_case convention with a clear verb_noun or transformation pattern (generate_image, inpaint_image, image_to_video, translate_video). The two image_to_* names follow a predictable transformation pattern, making the set uniform and intuitive.
With 9 tools, the server is well-scoped for an AI media generation platform. Each tool covers a distinct capability without redundancy, and the count falls comfortably within the ideal 3-15 range.
The tool surface covers the core media generation lifecycle: image creation/editing/upscaling, video generation from text or images, motion transfer, digital humans, and video translation. Minor gaps like audio generation or task management are not critical for the stated purpose, but the set is quite comprehensive.