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Getting Started with NanoBanana MCP in Claude Code

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Moving image work into a terminal sounds small until you are building docs, release notes, product mockups, or UI examples and keep losing context to browser tabs. NanoBanana MCP gives Claude Code a practical way to generate and edit images from the same project session where you already inspect files, write copy, and run commands. This guide walks through the setup described in the Ace Data Cloud documentation and shows how to think about NanoBanana as a developer workflow tool rather than a standalone image app. What you can do NanoBanana is useful when the image task depends on understanding existing visual content. The documentation highlights three common jobs: Image composition: pass multiple image URLs and ask the model to combine objects and scenes naturally. Image cleanup: remove unwanted text, such as a watermark or annotation, and fill the area with the surrounding background. Page illustration: generate small product or documentation illustrations directly...
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Getting Started with Nano Banana MCP for AI Image Workflows

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If your design or content workflow already lives inside an AI coding assistant, switching tabs to generate or edit images can break your flow. Nano Banana MCP is a practical way to bring image generation and editing into clients such as Claude Desktop, VS Code, and Cursor through the Model Context Protocol. What you can do The Nano Banana MCP Server from Ace Data Cloud exposes image tools directly to an MCP-compatible AI client. According to the integration guide, the server supports: Image generation from text prompts. Image editing for modifying existing images or combining multiple images. Virtual try-on , such as dressing clothing on photos of people. Product placement , such as placing a product into a real scene. Multi-model use with nano-banana , nano-banana-2 , and nano-banana-pro . Task querying to monitor generation progress and retrieve results. The nice part is that you do not need to build a custom UI before trying these workflows. Once the MCP...
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How to Generate and Edit Images with the Nano Banana Images API

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Image generation integrations can become difficult when an API must support both prompt-based generation, image editing, asynchronous delivery, and partial failures across multiple requested images. What you can do The Nano Banana Images API is available from the Ace Data Cloud API base URL: https://api.acedata.cloud Use the following endpoint to generate new images or edit existing ones: POST /nano-banana/images The API accepts two actions: generate creates images from a prompt. edit modifies one or more supplied source images using a prompt. Every request requires action and prompt . Editing requests additionally require image_urls , an array containing at least one image. Each image URL can be a publicly accessible direct HTTP or HTTPS link, or a Base64 data image. An application token is obtained from the Ace Data Cloud Console. One API token can call platform services. Send that token in the authorization header as a Bearer token. How it works Send a JSON request to...
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Поиск из Claude Code без потери контекста: практический MCP-workflow для инженера

Инженерный поиск во время отладки редко бывает отдельной задачей. Он возникает прямо в момент, когда в терминале появляется неизвестная запись ядра, ошибка CI, странный заголовок HTTP-ответа или регрессия после обновления зависимости. Если для каждого такого случая переключаться между редактором, браузером и документацией, теряется не только время: рвётся контекст расследования. MCP позволяет встроить поиск в среду, где уже находится код и журнал выполнения. Ниже — практический подход к использованию Google Search MCP вместе с Claude Code через Ace Data Cloud. Цель не в том, чтобы заменить инженерное мышление выдачей поиска, а в том, чтобы сделать поиск, сверку источников и фиксацию решения частью воспроизводимого процесса разработки. Что именно меняется в рабочем цикле Без интеграции типичный сценарий выглядит так: разработчик копирует фрагмент ошибки, открывает несколько вкладок, вручную отсеивает устаревшие советы, затем возвращается к проекту. При MCP агент получает возможност...
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Getting Started with Veo MCP in Codex CLI

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If you already use Codex CLI to reason about code, the awkward part of making short AI videos is not the prompt itself—it is leaving the terminal, opening another interface, copying assets around, and then trying to keep the result connected to the work you were doing. The Codex CLI Integration with Veo MCP document describes a small but useful setup: add the Veo MCP server to Codex so video generation becomes a tool you can call from the same conversation where you are planning a demo, writing release notes, or preparing a product walkthrough. What you can do With the Veo MCP server configured in Codex CLI, you can ask for video generation directly inside a Codex session. The documented capabilities are intentionally focused: Generate text-to-video with veo_text_to_video . Generate image-to-video with veo_image_to_video . Use Google DeepMind Veo 2, Veo 3, or Veo 3.1 from the same MCP integration. Work with options described by the documentation such as 1080P an...
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How to Build Image Generation and Editing Workflows with the Seedream Images API

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Building an image feature is not only about sending a prompt to a model. The harder part is designing a workflow that can generate, edit, stream, or poll results when a job takes longer than a single HTTP request. What you can do The Seedream Images API gives builders one endpoint for prompt-to-image generation, image editing with one or more input images, asynchronous jobs, streaming output, callback delivery, and Seedream 5.0 Pro layer decomposition. The core endpoint is POST https://api.acedata.cloud/seedream/images . In a typical request you pass a model , a prompt , and optionally fields such as image , size , watermark , response_format , output_format , stream , async , callback_url , tools , background , or layer_decomposition . How it works Send JSON with accept: application/json , authorization: Bearer YOUR_API_TOKEN , and content-type: application/json . The basic generation action uses action: generate , a full model string, and a prompt. The model must be the full stri...
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How to Build Reliable Image Editing Workflows with GPT Image 2

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Most image editing APIs are easy to demo once, but harder to turn into a reliable workflow: you need predictable inputs, constrained edits, clear error handling, and a way to avoid blocking your app while a large edit runs. This guide walks through a practical image editing flow using the Ace Data Cloud OpenAI Images Edits endpoint. The goal is not to generate random pictures from text. The goal is to take one or more existing images, describe a controlled edit, and receive a usable image result while keeping enough structure around the request to debug failures and scale the workflow later. What you can do The Images Edits API supports two main input styles: Edit from an image URL by sending JSON to https://api.acedata.cloud/openai/images/edits . Edit local images with multipart/form-data , including optional mask-based local edits for supported :official models. The core fields are intentionally small: model , image , prompt , and optional controls such as size , ...
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