"Docker" matching MCP servers:

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

Enables AI image generation using FLUX models through ComfyUI with GPU acceleration, supporting image generation, 4x upscaling, and background removal with optimized Docker deployment.

Gives an agent a full video editing room: import footage, cut and order shots, add captions and audio, composite, replace screens via SAM2, render, and export in multiple formats, all locally in Docker with no API key required.

Adds image understanding to AI assistants that lack native vision, letting them analyze code screenshots, error captures, UI layouts, documents, and OCR content through a single unified image_understand tool. It routes requests across multiple vision model providers such as GLM-4.6V, DeepSeek-OCR, Qwen3-VL, Doubao-Seed, and Hunyuan, handling large-image cropping, compression, retries, and HTTP/Docker deployment.

Provides a unified image-understanding tool for AI coding assistants without native vision, letting them analyze screenshots, error messages, UI layouts, and documents through multiple vision LLM providers. Supports local files, remote URLs, and Data URIs with preprocessing, OCR, multi-crop, retries, and HTTP/Docker deployment.

Enables AI coding clients that lack native vision to analyze screenshots of code, errors, UIs, and documents by routing images through a single image_understand tool backed by multiple vision LLM providers such as GLM-4.6V, DeepSeek-OCR, Qwen3-VL-Flash, Doubao-Seed, and Hunyuan-Vision. Handles local files, remote URLs, and Data URIs with automatic multi-crop, compression, retry, and OCR-friendly preprocessing, and can be deployed over stdio or shared HTTP/Docker for multiple clients.

Enables LLM agents to drive a self-hosted video toolkit through MCP tools, including lipsync, face restoration, ffmpeg operations, and metadata retrieval.

Enables AI clients and coding models without native vision to analyze images — screenshots, code errors, UI layouts, documents and OCR — through a single image_understand tool. Routes requests to multiple vision model providers with automatic preprocessing, multi-crop, compression, retry, and optional HTTP/Docker deployment for shared access.

A multi-model vision MCP server that gives image understanding to AI coding models lacking native vision, letting them analyze screenshots of code, errors, UI, and documents or perform OCR. It routes requests through major vision LLM providers and can be shared over local networks via HTTP or Docker.

Enables AI coding assistants without native vision to analyze images such as screenshots, error messages, UI mockups, and documents through a single image_understand tool backed by multiple mainstream vision model providers. It handles local files, remote URLs, and Data URIs with automatic cropping, compression, and retries, and can be deployed locally via stdio or shared over HTTP/Docker.

Enables AI assistants and coding models without native vision to analyze images—screenshots, error messages, UIs, documents and OCR text—through a single unified image_understand tool. Supports multiple vision LLM providers (GLM-4.6V, DeepSeek-OCR, Qwen3-VL, Doubao, Hunyuan, or any OpenAI-compatible endpoint), with automatic image preprocessing, cropping and retries, over stdio or shared HTTP/Docker deployment.

Provides read-only access to Bilibili through 25 MCP tools for videos, subtitles, danmaku, comments, Opus, user profiles, feeds, public favorites, video collections, live rooms, public crew lists, images, and video frame capture. Supports single-account QR-code login and Docker Compose deployment.

A Docker-containerized MCP proxy that provides AI image generation, text generation, vision analysis, and text-to-speech capabilities through REST endpoints using Pollinations AI services. Enables multimodal AI interactions including image creation, transformation, OCR, and audio generation through standard HTTP APIs.

Enables AI coding models that lack native vision to analyze screenshots, error messages, UI layouts, documents, and OCR-heavy images by routing each request to one of several major vision LLM providers through a single unified tool. Handles local files, remote URLs, and Data URIs with automatic compression, multi-cropping for large images, retries, and optional HTTP/Docker deployment for shared access.

A Docker-based deployment that wraps the Gemini MCP server with supergateway to expose its tool capabilities over Streamable HTTP. It enables users to perform image-editing workflows and interact with the Google Gemini API using a standardized web-accessible endpoint.