image-gen-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| IMAGE_API_KEY | Yes | Your API Key (required) | |
| IMAGE_BASE_URL | No | Custom API URL for custom provider | |
| IMAGE_MCP_HOST | No | SSE listen host | 0.0.0.0 |
| IMAGE_MCP_PORT | No | SSE listen port | 8000 |
| IMAGE_PROVIDER | No | Select provider: siliconflow, openai, or custom | siliconflow |
| DEFAULT_IMAGE_SIZE | No | Default image size | 1024x1024 |
| DEFAULT_IMAGE_MODEL | No | Default model to use | black-forest-labs/FLUX.1-dev |
| IMAGE_MCP_TRANSPORT | No | Transport mode: stdio or sse | stdio |
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 | 根据文字描述生成一张或多张图片。 向图片生成模型发送文字描述（prompt），模型会生成对应的图片并返回图片 URL。 支持多个提供商，默认使用硅基流动（免费额度）。 model 参数不传时使用默认模型。不同提供商默认模型不同：
你也可以传入自己熟悉的任意模型名称（只要在你的提供商平台可用）。 size 参数控制图片尺寸：
适用于：
注意：生成的图片 URL 通常有有效期（数小时到数天），请及时保存。 |
| list_image_modelsB | 列出当前提供商推荐使用的图片生成模型列表及使用说明。 |
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 2 tools
The two tools have completely distinct purposes: one lists available image models, the other generates images. No overlap or confusion possible.
Both tools use a consistent verb_noun pattern in snake_case: 'list_image_models' and 'generate_image', making them predictable and easy to understand.
With only 2 tools, the server feels under-scoped for its stated support of multiple providers and model selection. While minimal, it may be acceptable for a focused image generation task, but lacks coverage for related operations.
The server covers basic listing and generation, but lacks tools for retrieving generated images (URLs expire), managing providers, or handling model customization beyond the generate call. Minor gaps exist that could limit agent autonomy.