AceDataCloud

Multi-image editing becomes much more useful when it can stay inside the same terminal workflow where you are already writing docs, adjusting product copy, or preparing release assets. What you can do The NanoBanana MCP server is designed for image generation and image editing workflows inside OpenCode. The documented strengths are practical rather than abstract: product background replacement, virtual try-on, subject-consistent edits for people or objects, and editing one image with another image as a reference. In an OpenCode session, that means you can ask for tasks such as: placing a product image into a scene while keeping lighting consistent; changing a model image to wear a referenced garment while preserving face and pose; generating a small illustration for a 404 page or documentation page; using several source images as references without leaving the terminal. How it works OpenCode reads MCP servers from an opencode.json file. Ace Data Cloud exposes Nan...

When you are already working in a terminal, switching to a browser just to generate or edit a product image breaks flow. A practical alternative is to connect an image MCP server to Claude Code, then ask for image work in the same place where you read code, edit files, and run commands. This guide walks through the NanoBanana MCP setup for Claude Code using the public Ace Data Cloud documentation. The goal is not to replace your design process; it is to give your coding agent a callable image tool for common builder tasks: README visuals, landing page illustrations, product mockups, and multi-image composition. What you can do NanoBanana is useful when the task needs image understanding, not only fresh image generation. The documentation describes it as a tool that can understand relationships across images—for example, placing an item from one image into the scene of another and adjusting angle and lighting so the result looks natural. In Claude Code, the NanoBanana MCP server ex...

Когда разработчик разбирает сбой на удалённой машине, изучает незнакомый пакет или готовит архитектурное решение, самый дорогой ресурс — не сам поисковый запрос, а потеря контекста. Логи остаются в терминале, фрагменты конфигурации — в репозитории, а найденные материалы — в браузере. Связка Claude Code и поискового MCP помогает удержать эту цепочку в одной рабочей сессии: агент получает задачу, формулирует запросы, возвращает ссылки и превращает результаты в проверяемые действия. Эта статья описывает инженерный подход к такой интеграции: как выбрать область конфигурации, как не раскрыть секрет, как проверять соединение и как добавить обычный API-вызов в скрипт автоматизации. Она опирается на руководство Ace Data Cloud для Claude Code и Google Search MCP. Сначала создайте приложение и API Token в консоли приложений ; обзор платформы доступен на русской странице Ace Data Cloud , а актуальные инструкции — в каталоге документации . Что именно добавляет MCP в рабочий процесс Claude Cod...

When you are already working inside a terminal agent, the slow part of an image workflow is often not the model call itself. It is switching tools, copying URLs, explaining context again, and then bringing the result back into your project. NanoBanana MCP gives Claude Code a direct image generation and editing toolchain from the same terminal session, so you can ask for a visual asset, compose several source images, or clean up an image without leaving the coding flow. What you can do The NanoBanana MCP documentation describes a focused setup for Claude Code: connect the remote MCP server once, then call two tools from natural language: nanobanana_generate_image for text-to-image generation. nanobanana_edit_image for image editing, including workflows that pass multiple image URLs. That makes it useful for small but common builder tasks: creating a 404-page illustration, placing an object from one image into the scene of another image, or removing unwanted text from a sc...

Video generation becomes much easier to prototype when it sits inside the tools where builders already write prompts, inspect assets, and iterate on ideas. The Veo MCP Server from Ace Data Cloud lets an MCP-capable client such as Claude Desktop, VS Code, or Cursor call Veo video tools through a standard interface, so you can move from a written brief to a generated video task without leaving your working environment. What you can do The documented Veo MCP setup exposes a small, practical tool surface for video workflows: veo_text_to_video for generating video from text prompts. veo_image_to_video for generating video based on images. veo_get_1080p for upgrading an already generated video to 1080p. veo_get_task for checking a single task status. veo_get_tasks_batch for checking multiple task statuses. The same document also describes support for multiple Veo models, including veo3 , veo2 , and veo31-fast-ingredients , plus output options such as 4K, 1080p, GIF...

If you are building a CLI, backend worker, or agent that calls AI APIs on behalf of different users, API keys are often the awkward part: who owns the balance, who rotates the token, and how do you charge only for one call? The paymentHandler hook in the Ace Data Cloud SDK gives you another path: let the first request receive 402 Payment Required , sign an X402 payment envelope locally, then retry the same API call with a PAYMENT-SIGNATURE header. What you can do The documented flow is useful when you want business code to stay close to a normal SDK call while payment happens per request. In practice, you can: Call client.openai.chat.completions.create(...) without passing an apiToken . Inject a TypeScript or Python paymentHandler that signs the payment challenge after a 402 . Use EVM networks such as base or skale , or use solana where the Solana signer path is supported. Choose preferScheme: 'upto' / prefer_scheme="upto" for metered chat-st...