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How to Edit Images from Cursor with NanoBanana MCP

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If you build landing pages, docs, demos, or small product experiments, image work often becomes the awkward handoff: you know exactly what you want changed, but opening a design tool breaks your coding flow. This guide shows how to wire NanoBanana MCP into Cursor so you can generate and edit images from Agent mode using the same natural-language workflow you already use for code. The goal is not to replace a designer. It is to remove the repetitive image tasks that slow down builders: cleaning up screenshots, composing product mockups, and creating small illustrations for app states. What you can do The Ace Data Cloud documentation describes NanoBanana as an image tool focused not only on creating new images, but also on modifying existing images. In practical terms, the Cursor MCP integration gives you two tools: nanobanana_generate_image — text-to-image generation. nanobanana_edit_image — image editing, including multiple image inputs. That second capability is the mo...
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How to Build an Image Generation and Editing Workflow with the Nano Banana Images API

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If your product needs both image generation and image editing, the annoying part is usually not the model itself. It is the workflow around it: accepting a prompt, optionally attaching reference images, tracking the result, and making the output usable inside your own app. The Nano Banana Images API gives you a single HTTP endpoint for that workflow: POST /nano-banana/images . This guide walks through a practical integration pattern: start with synchronous calls for development, keep task_id and trace_id for debugging, then move longer-running jobs to a webhook using callback_url . What you can do The API supports two actions on the same endpoint: generate : create images from a text prompt . edit : transform or combine existing images using image_urls plus a text prompt . That makes it useful for builder workflows such as product mockups, creative previews, image variations, avatar or asset generation, and reference-based editing. You can request count from 1 to 4 ima...
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A Practical Guide to Image Generation and Editing with the Nano Banana Images API

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If your product workflow needs to create images from prompts and also edit existing assets, the hard part is usually not the model itself — it is building a predictable API flow around prompts, source images, callbacks, and result tracking. This guide walks through the Nano Banana Images API on Ace Data Cloud as a practical image generation and editing endpoint. We will focus on the parts a builder actually needs: the endpoint, required fields, generation vs. editing, callbacks, and how to keep enough IDs around to debug failed or delayed jobs. What you can do The Nano Banana Images API exposes one HTTP endpoint for two related actions: generate : create images from a text prompt . edit : edit or combine one or more existing images using image_urls plus a text prompt . The API is useful when you want one integration path for common product image tasks: generating a blog illustration, creating a product concept, adapting a reference image, or combining a person image with another...
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How to Build Image Generation and Editing Workflows with the Nano Banana Images API

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If you are building an app that needs both text-to-image generation and image-to-image editing, the annoying part is often not the model call itself. It is keeping the request shape simple enough for product code, while still supporting reference images, async callbacks, and traceable results. The Nano Banana Images API gives you one endpoint for both workflows: generate a new image from a prompt, or edit one or more existing images with a prompt. What you can do The core capability is intentionally compact. You call POST /nano-banana/images on the base URL https://api.acedata.cloud , authenticate with authorization: Bearer {token} , and choose an action . action: generate creates images from a text prompt . action: edit modifies or combines existing images passed through image_urls . count requests 1 to 4 images. If some images fail, only the successful images are returned and billed. callback_url lets your app receive the result later instead of keeping a lon...
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Getting Started with Claude Code in VS Code Through Ace Data Cloud

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If you already live in VS Code, the friction in using an agentic coding tool is usually not the model itself — it is getting the editor, project context, file diffs, and approval flow to feel natural. Claude Code’s VS Code extension gives you that native workflow, and Ace Data Cloud can be configured as the API base so the extension talks to https://api.acedata.cloud instead of asking you to complete the default Anthropic login flow. What you can do With this setup, Claude Code runs inside VS Code or Cursor as an editor-native coding agent. The documented workflow supports a graphical panel, inline diff review, file and directory references with @ -mentions, slash commands, conversation history, and several permission modes for controlling how changes are applied. Ask questions about the current codebase in natural language. Reference files such as @filename.py or directories such as @src/components/ . Use @terminal:name to bring terminal output into the conversation....
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How to Use Codex CLI with Ace Data Cloud in Your Terminal

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When a coding agent runs in your terminal, the hard part is not the chat interface. It is making sure the CLI reads the right provider configuration, sends requests to the right API protocol, and behaves safely inside each project. This guide walks through configuring Codex CLI to use Ace Data Cloud as an OpenAI Responses-compatible provider, while keeping the native codex terminal workflow intact. What you can do Codex CLI is a local programming agent that runs from the terminal. According to the Ace Data Cloud documentation, it can read code, modify files, execute commands, explain errors, and assist with everyday development tasks. The useful part for builders is that Codex CLI supports custom model providers, so you can point the CLI at Ace Data Cloud instead of changing your workflow or adding a separate local proxy. Once configured, the CLI sends requests to https://api.acedata.cloud/v1 . For the Responses protocol specifically, the effective endpoint is https://api.acedat...
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A Practical Guide to Running Claude Code from the Terminal with AceData Cloud

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When you are working inside a real codebase, the fastest AI workflow is often not another browser tab. It is a terminal command that can read the project, inspect diffs, explain code, write tests, and help you commit changes without leaving your shell. This guide walks through a practical Claude Code CLI setup using AceData Cloud as the API proxy. The goal is simple: install claude , point it at https://api.acedata.cloud , and start using it for everyday builder tasks such as bug fixing, review, testing, and project memory. What you can do The Claude Code terminal experience is designed around natural language inside your project directory. Once configured, you can run the claude command in a terminal and ask it to inspect the repository, explain a module, fix a bug, write unit tests, refactor code, review a diff, or create a Git commit. The documented CLI supports several useful entry points: claude starts an interactive coding session. claude "task" run...
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