gpt-image-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OPENAI_API_KEY | Yes | OpenAI API key. Required. | |
| IMAGE_GEN_MODEL | No | Image generation model. | gpt-image-1 |
| OPENAI_BASE_URL | No | OpenAI API base URL. | https://api.openai.com/v1 |
| IMAGE_GEN_OUTPUT_DIR | No | Directory where generated images will be saved. | ./output |
| IMAGE_GEN_TIMEOUT_MS | No | Request timeout in milliseconds. | 300000 |
| IMAGE_GEN_RESPONSE_FORMAT | No | Response format for image generation: 'b64_json' or 'url'. | b64_json |
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": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| check_endpointA | 请求当前端点的模型列表，检查响应格式和模型是否在列表中。不会发送生图或编辑请求；即使检查通过，图片能力仍需实际调用验证。 |
| generate_imageA | 根据提示词调用 GPT Image 生成一张图片，保存到本机并返回绝对路径和文件 URI。调用会产生 API 费用。 |
| edit_imageA | 读取本机图片，按提示词编辑、替换背景，或参考风格和构图生成新图。请明确各参考图的作用和需要保留的内容；可传遮罩引导局部编辑。保存新文件并返回绝对路径，不覆盖原图。调用会产生 API 费用。 |
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 3 tools
Each tool has a clearly distinct purpose: check_endpoint validates API connectivity, generate_image creates new images, and edit_image modifies existing images. There is no meaningful overlap between these operations, so an agent can confidently select the right tool.
All tool names follow the same verb_noun snake_case pattern: check_endpoint, generate_image, edit_image. This makes the tool set highly predictable and easy to reason about.
Three tools is well-scoped for a focused GPT Image MCP server: endpoint validation, generation, and editing cover the core functionality without unnecessary bloat. Each tool earns its place.
The set covers the primary image lifecycle: checking endpoint availability, generating images, and editing existing images. Minor gaps exist, such as no explicit model listing or image variation/upscale capabilities, but the core workflows are complete enough for most use cases.