Click on "Install 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., "@Volcengine Image Generation MCP Server generate a futuristic cityscape at night with flying cars, size 1280x720"
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.
A Model Context Protocol (MCP) server that provides tools for generating images using Volcengine's text-to-image API.
Text-to-image generation using Volcengine's doubao-seedream-3-0-t2i-250415 model
Support for multiple image sizes and formats
Configurable parameters including seed, guidance scale, and watermark
Proper error handling and validation
TypeScript implementation with full type safety
Related MCP server: Doubao Image/Video Generation MCP Server
Clone or download this repository
Install dependencies:
npm installBuild the project:
npm run buildSet the required environment variable:
export VOLCENGINE_API_KEY="your_api_key_here"npm startnpm run devGenerate images from text prompts using Volcengine's API.
Parameters:
prompt (required): Text description of the image to generate
model (optional): Model ID (default: doubao-seedream-3-0-t2i-250415)
size (optional): Image size (default: 1024x1024)
seed (optional): Random seed for reproducible results
guidance_scale (optional): How closely to follow the prompt (1-10)
watermark (optional): Whether to add watermark (default: true)
response_format (optional): Return format - 'url' or 'b64_json' (default: url)
Supported sizes:
1024x1024 (1:1)
864x1152 (3:4)
1152x864 (4:3)
1280x720 (16:9)
720x1280 (9:16)
832x1248 (2:3)
1248x832 (3:2)
1512x648 (21:9)
{
"name": "generate_image",
"arguments": {
"prompt": "A beautiful sunset over mountains",
"size": "1024x1024",
"guidance_scale": 7.5
}
}Add this server to your MCP client configuration:
{
"mcpServers": {
"volcengine-image": {
"command": "node",
"args": ["path/to/build/index.js"],
"env": {
"VOLCENGINE_API_KEY": "your-api-key"
}
}
}
}The server provides comprehensive error handling for:
Invalid API keys
Rate limiting
Invalid parameters
Network errors
API response errors
MIT
Generate images using Volcengine's text-to-image API
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Text prompt for image generation (max 1000 characters) | |
| model | No | Model to use for image generation | doubao-seedream-3-0-t2i-250415 |
| size | No | Image size dimensions | 1024x1024 |
| seed | No | Random seed for reproducible results (-1 for random) | |
| guidance_scale | No | How closely to follow the prompt (1-10) | |
| watermark | No | Whether to add watermark to generated images | |
| response_format | No | Format for returned image data | url |
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 mentions the API provider but doesn't describe rate limits, authentication requirements, error handling, what happens when generation fails, or the nature of the output (e.g., image format, quality constraints). This leaves significant gaps for a generative 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 a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It's perfectly front-loaded and wastes no space.
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?
For a generative image tool with 7 parameters and no output schema, the description is minimal. While concise, it doesn't compensate for the lack of annotations or output schema by explaining what kind of image data is returned, typical use cases, or limitations. The schema handles parameter documentation well, but overall context remains incomplete.
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 description adds no parameter-specific information beyond what's already in the schema, which has 100% coverage with detailed descriptions for all 7 parameters. This meets the baseline of 3 since the schema does the heavy lifting, but the description doesn't provide additional context like typical prompt structures or model selection guidance.
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 action ('Generate images') and specifies the resource ('using Volcengine's text-to-image API'), making the purpose immediately understandable. However, since there are no sibling tools mentioned, it doesn't need to distinguish from alternatives, so it can't achieve a perfect 5.
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 no guidance on when to use this tool versus alternatives, prerequisites, or typical use cases. It simply states what the tool does without any contextual usage information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
With only one tool, there is no possibility of confusion or overlap between tools. The single tool 'generate_image' has a clear and distinct purpose for generating images via a text-to-image API, leaving no ambiguity for an agent to misselect.
The tool name 'generate_image' follows a consistent verb_noun pattern, and with only one tool, there is no inconsistency to evaluate. The naming is straightforward and predictable, adhering to common conventions without any deviations.
A single tool is too few for a server focused on image generation, as it lacks essential operations like listing generated images, managing styles, or handling errors. This minimal scope limits functionality and feels incomplete for the domain, making it borderline inappropriate.
The tool set is severely incomplete for an image generation server. While 'generate_image' covers the core creation function, there are significant gaps such as retrieving, updating, or deleting images, managing parameters, or handling batch operations, which will likely cause agent failures in complex workflows.
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If you are the server author, to access and configure the admin panel.
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