"Creating custom experimental images in Docker" matching MCP servers:

Enables generating images through SmartAPI's image generation endpoint directly from Codex, saving files locally.

Provides image generation tools powered by OpenRouter, running on Cloudflare Workers.

Enables local image generation through a ChatGPT web account, downloading original files with paths, sizes, and checksums while keeping responses compact. Supports batched requests and resumable recovery without embedding images in model context.

An MCP server for generating and editing images using OpenAI's GPT Image and DALL·E models. It provides tools for image generation, editing, variation creation, and model listing with support for multiple AI models.

Enables MCP-compatible coding agents to generate and edit images from text prompts or local files, returning both a saved file path and an inline image block. Supports up to four sequential images per call, model listing, and one-command setup across OMP, Codex, Claude Code, Cursor, and opencode.

Enriches Claude with tools to generate brand-consistent images using OpenAI's gpt-image-2 and upload them directly to Notion pages.

Enables generating and editing images with OpenAI's gpt-image-2 directly from Claude or ChatGPT, including uploads, masks, composition, listing, deletion, and spend tracking through a self-hosted Cloudflare Workers connector.

Exposes OpenAI's gpt-image-2 and gpt-image-1.5 to Claude Code as a single tool for generating, editing, or composing images with automatic model selection.

Call OpenAI Codex from Claude Code for independent second opinions, structured code review, and delegated coding tasks through a FastMCP plugin that drives the codex CLI safely.

Enables users to describe their LLM fine-tuning job once and get the cheapest, fastest, and most balanced GPU options across a dozen cloud providers in seconds.

Enables Codex to delegate code reviews, coding tasks, image/video generation, and background investigations to Grok via MCP tools.

Enables private, GPU-backed image upscaling via a local Hermes MCP bridge that submits jobs to an authenticated Upscayl worker API.

Enables users to describe an LLM fine-tuning workload and receive ranked, cost- and runtime-optimized GPU recommendations across multiple cloud providers by estimating VRAM and FLOPs.

Enables users to describe an LLM fine-tuning job once and receive the cheapest, fastest, or most balanced GPU hardware options across a dozen cloud providers by calculating required VRAM/FLOPs and aggregating live pricing.

Enables users to describe an LLM fine-tuning job and receive ranked, task-aware GPU recommendations with estimated VRAM/FLOPs, cost, and runtime across more than ten cloud providers.

Enables users to describe an LLM fine-tuning workload once and get task-aware VRAM/FLOPs estimates plus ranked cheapest, fastest, and balanced GPU recommendations across multiple cloud providers.

Enables users to describe an LLM fine-tuning job and receive the cheapest, fastest, or most balanced GPU options across multiple cloud providers by calculating required VRAM and FLOPs.

Enables users to describe an LLM fine-tuning job and receive ranked GPU recommendations across a dozen cloud providers based on estimated VRAM/FLOPs, live pricing, cost, and speed. It returns cheapest, fastest, and balanced hardware options without manual comparison.

Enables users to describe an LLM fine-tuning workload once and get ranked, cost- and speed-optimized GPU recommendations across multiple cloud providers by estimating VRAM/FLOPs requirements.

Enables users to describe an LLM fine-tuning job once and get ranked GPU hardware recommendations with estimated VRAM/FLOPs, cost, and runtime across a dozen cloud providers.