GPT Image MCP

GPT Image MCP

Generates and edits images using OpenAI image models via MCP tools.

Category
访问服务器

README

GPT Image MCP

FastAPI + FastMCP service for generating and editing images with OpenAI image models.

Python Docker License CI

What you get

  • MCP tools: generate_image, edit_image, health
  • Streamable HTTP MCP transport at POST /mcp/
  • HTTP health check at GET /health
  • Static image hosting at GET /images/{filename}
  • Optional bearer auth for MCP and image routes
  • Upload validation, retry logic, structured logs, and image cleanup

MCP tools

  • generate_image creates a new image from text.
  • edit_image edits one or more images and supports an optional mask.
  • health returns a simple service status payload.

Environment variables

Copy the example file first:

cp .env.example .env

Required:

  • OPENAI_API_KEY - OpenAI API key

Optional:

  • PUBLIC_URL - public base URL for returned image links
  • MCP_API_KEY - bearer token required for /mcp/ and /images/*
  • IMAGE_DIR - image storage directory
  • LOG_LEVEL - logging level, default INFO
  • MAX_UPLOAD_MB - max size for uploaded images, default 50
  • MAX_MASK_MB - max size for masks, default 4
  • MAX_IMAGE_EDGE_PX - max image edge length, default 8192
  • IMAGE_RETENTION_DAYS - cleanup retention window, default 7
  • CLEANUP_INTERVAL_SECONDS - cleanup interval, default 86400
  • OPENAI_RETRY_ATTEMPTS - retry count for OpenAI image calls, default 3
  • OPENAI_RETRY_BASE_DELAY_SECONDS - retry backoff base, default 0.75
  • REQUEST_TIMEOUT_SECONDS - timeout for remote image downloads, default 60

Run locally

pip install -r requirements.txt
uvicorn app.main:app --host 0.0.0.0 --port 8080

The MCP endpoint is available at:

  • http://localhost:8080/mcp/

Run with Docker

docker compose up --build

Tests

python3 -m unittest discover -s tests -p "test*.py" -v

If you have pytest installed:

pytest -q

Verify on GitHub

git remote -v
git push -u origin main

If you have not connected the repository yet:

git branch -M main
git remote add origin git@github.com:<your-username>/<your-repo>.git
git push -u origin main

Notes

  • POST /mcp/ is the MCP transport endpoint expected by LobeHub.
  • generate_image and edit_image return the same response shape.
  • edit_image accepts image files stored under IMAGE_DIR or public http(s) URLs.
  • Local paths outside IMAGE_DIR and private or localhost remote URLs are rejected for security.
  • Returned image URLs use PUBLIC_URL when it is set, otherwise they fall back to /images/{filename}.
  • Old images are cleaned up automatically according to IMAGE_RETENTION_DAYS.

Troubleshooting

  • 401 Unauthorized
    • Set MCP_API_KEY on the server and send Authorization: Bearer <token> from the client.
    • If you are using LobeHub or nginx, make sure the proxy forwards the Authorization header.
  • 404 Not Found on /mcp/
    • Use the trailing slash: POST /mcp/.
    • Confirm nginx proxies to the local container port and does not rewrite the path.
    • Verify the FastMCP server is mounted through app.main:app.
  • Invalid image or Unsupported image
    • Use a real PNG, JPEG, or WEBP input image.
    • For masks, use PNG only and keep the mask the same size as the first input image.
    • Check that the file is not corrupted and is smaller than MAX_UPLOAD_MB or MAX_MASK_MB.
  • File not found or Remote image host errors
    • Keep local file references under IMAGE_DIR.
    • Use public, reachable http(s) URLs only.
  • OpenAI API errors
    • Confirm OPENAI_API_KEY is set and valid.
    • Check rate limits, quota, and model availability.
    • Verify the server has outbound network access to OpenAI.
  • Image URL is not reachable
    • Set PUBLIC_URL to the external domain that serves the service.
    • Make sure nginx serves /images/ correctly and the image directory is mounted into the container.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选