Image Generator MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OPENAI_API_KEY | Yes | Your API key for the OpenAI-compatible endpoint. | |
| OPENAI_API_BASE | Yes | The base URL of your OpenAI-compatible API endpoint. |
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 | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generate_imageB | 文生图：Generate an image from a text prompt using high-quality AI models. Uses gpt-image-2.5-sunburst by default (最佳性价比 - 实测噪点最低). Available models: gpt-image-2.5-sunburst, gpt-image-2.5-flare, gpt-image-2 |
| edit_imageB | 图生图/局部编辑：Edit an existing image based on a prompt. Can modify specific areas while keeping others unchanged. Supports inpainting with mask (修改指定区域，其他保持不变). |
| create_variationB | 图生图变体：Create variations of an existing image. Keeps the overall style and composition while generating new versions (保持风格和构图，生成变体). |
| list_imagesA | 检索图片：Search and list generated images by tags, date, or keywords. Returns matching images with metadata. |
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 4 tools
The four tools mostly target distinct actions: text generation, local editing, variation creation, and image retrieval. The main possible confusion is between edit_image and create_variation, since both operate on an existing image, but their descriptions clarify that one performs targeted/masked edits while the other generates stylistic variants.
All tool names use snake_case and follow a clear verb_noun pattern: edit_image, create_variation, list_images, generate_image. The verbs differ appropriately by action, and the naming is predictable throughout.
Four tools is well-scoped for a focused image generation server. Each tool covers a distinct core operation without excessive surface area.
The server covers the main image lifecycle operations: generate, edit, create variations, and list/search images. Minor gaps exist, such as explicit get_image-by-ID or delete_image operations, but core generation and editing workflows are covered.