A
licenseA
qualityD
maintenanceEnables image analysis using any OpenAI-compatible vision API, supporting URLs, local files, or base64 input with custom prompts.
1
MIT
Doubao Vision MCP Server
by kira4094
This server provides Doubao-powered multimodal capabilities via Volcengine Ark, using a single API key.
Image understanding (
doubao_vision_understand): Analyze local or URL images with a text prompt, adjustable detail (auto/low/high),max_tokens, andtemperature.Image generation (
doubao_seedream_generate): Generate images from text prompts or reference images, with configurable size, aspect ratio, and number of images.Video generation (
doubao_seedance_generate): Asynchronously submit text-to-video or image-to-video tasks; returns atask_idfor polling.Video status query (
doubao_seedance_query): Poll task status bytask_idand retrieve a download URL when succeeded.Server behavior can be configured through environment variables for vision, image, and video models, supporting preset or custom endpoints (
ep-xxxxx).
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Doubao Vision MCP Server describe the objects and colors in this image: https://example.com/photo.jpg"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Doubao Vision MCP Server
火山方舟（Volcengine Ark）豆包系列 MCP Server：视觉理解 + Seedream 图像生成 + Seedance 视频生成，一套密钥全打通。
特性
🖼️ 4 个工具：视觉理解 / 图像生成 / 视频提交 / 视频轮询
🎬 Seedream 同步出图：图像几秒返回，无需轮询
🎥 Seedance 两段式：异步提交 task_id → 轮询拿视频（MCP stdio 不超时）
💬 自定义 prompt，支持中文/英文
🔄 所有模型可用环境变量切换，代码零改动
⚡ 一行 npx 部署
💰 支持火山方舟赠送的免费 token
Related MCP server: Vision MCP Server
支持的模型
视觉理解
项目 | 说明 |
模型 ID |
|
接入点 | 预置推理接入点（无需创建，直接填模型名） |
模态 | 文本 + 图片 + 语音 + 视频 |
图像生成（Seedream）
项目 | 说明 |
模型 ID |
|
图生图 |
|
备选 |
|
视频生成（Seedance）
项目 | 说明 |
模型 ID |
|
备选 |
|
⚠️ 注意 | 需在火山方舟控制台开通对应模型； |
环境变量
变量 | 必填 | 默认值 | 说明 |
| ✅ | — | 火山方舟 API Key。获取：控制台 → API Key 管理 |
| 否 |
| 视觉模型名（预置接入点）或 ep-xxxxx |
| 否 |
| Seedream 文生图模型 |
| 否 |
| Seedream 图生图模型（传 |
| 否 |
| Seedance 视频生成模型 |
| 否 |
| API 地址，通常无需修改 |
开通模型（重要）
在 火山方舟控制台 → 开通管理 开通需要的模型（通常有免费试用额度）：
图像生成：
Doubao Seedream系列视频生成：
Doubao Seedance系列（建议 2.0 / 2.5）
快速开始
npx 部署
{
"mcpServers": {
"doubao-vision": {
"command": "npx",
"args": ["-y", "doubao-vision-mcp-server"],
"env": {
"ARK_API_KEY": "ark-xxxxxxxxx-xxxxx"
}
}
}
}自定义模型
{
"mcpServers": {
"doubao-vision": {
"command": "npx",
"args": ["-y", "doubao-vision-mcp-server"],
"env": {
"ARK_API_KEY": "ark-xxxxxxxxx-xxxxx",
"SEEDREAM_MODEL": "doubao-seedream-5-0-pro-260628",
"SEEDANCE_MODEL": "doubao-seedance-2-5-260628"
}
}
}
}工具
doubao_vision_understand — 视觉理解
参数 | 必填 | 默认值 | 说明 |
| ✅ | — | 本地图片路径 |
| ✅ | — | 对图片的指令，越具体越好 |
| 否 |
| 图片精度： |
| 否 |
| 最大输出 token 数 |
| 否 |
| 采样温度（0~2） |
doubao_seedream_generate — 图像生成（同步）
参数 | 必填 | 默认值 | 说明 |
| ✅ | — | 图像描述（中英文皆可） |
| 否 | — | 参考图（本地路径/URL），传了即图生图 |
| 否 | — | 分辨率，如 |
| 否 | — | 宽高比： |
| 否 |
| 生成数量 |
doubao_seedance_generate — 视频生成（异步提交）
参数 | 必填 | 默认值 | 说明 |
| ✅ | — | 视频描述 |
| 否 | — | 参考图（图生视频） |
| 否 | — | 分辨率（如 |
| 否 | — | 时长（秒，模型支持范围 4~30） |
返回 task_id，随后用 doubao_seedance_query 轮询。
doubao_seedance_query — 视频生成（轮询）
参数 | 必填 | 说明 |
| ✅ |
|
返回 status（queued / running / succeeded / failed），succeeded 时给出视频下载 URL。
使用示例
分析这张 UI 截图：
C:\screenshot.png，描述它的布局和配色方案
生成一张暖色调复古酒吧氛围图，两个人西装对话，电影胶片质感
用这张参考图生成一段 5 秒视频：角色转头看向镜头，微笑（会返回 task_id，再查询）
本地开发
git clone https://github.com/kira4094/doubao-vision-mcp-server.git
cd doubao-vision-mcp-server
npm install
node src/index.js验证结果
能力 | 模型 | 结果 |
视觉理解 |
| ✅ |
图像生成（文生图） |
| ✅ 实测出图 |
图像生成（图生图） |
| ✅ 模型已开通 |
视频生成 |
| ⏳ 需控制台开通后启用 |
关联项目
glm-vision-mcp-server — 智谱 GLM 视觉模型 MCP
agnes-image-mcp-server — Agnes 图像生成 MCP
agnes-video-mcp-server — Agnes 视频生成 MCP
Available Tools
1 tooldoubao_vision_understandC
Analyze an image using Doubao vision model via Volcengine Ark API. Supports both preset inference (model name) and custom inference (ep-xxxxx endpoint ID). Configure via DOUBAO_MODEL environment variable.
Inputs: image (required), prompt (required), detail, max_tokens, +1 more.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
B3.2/5.0
Scored across 1 tool
Disambiguation5/5
Only one tool exists, so there is no ambiguity or overlap with other tools.
Naming Consistency5/5
With a single tool, naming consistency is not applicable; the name is clear and descriptive.
Tool Count3/5
One tool is on the low end of the range, but it is focused and covers the core image analysis task for a specialized vision server.
Completeness3/5
The tool covers image analysis, but the server lacks additional operations like image generation or batch processing that might be expected in a vision server.
Maintenance
ActivityInactive
ResponsivenessOutdated data
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