Teaching Google Antigravity to Paint: A Stateful Image-Editing Skill Built on Gemini's Interactions API and MCP

A developer built a stateful image-editing skill for Google Antigravity CLI by wrapping Google's gemini-3.1-flash-lite-image model (NB2Lite) in a FastMCP server. The skill leverages Gemini's Interactions API to allow iterative edits without re-prompting the entire scene, enabling workflows like generating a cyberpunk kitchen and then adding a neon RAMEN sign to the same image. The project ships as an MCP server exposing four tools for generating and editing images, with Antigravity CLI as the client.

TL;DR: nb2lite-skill-agy wraps Google's gemini-3.1-flash-lite-image model NB2Lite in a FastMCP server and packages it as an Antigravity CLI skill. You type "generate an image of a cyberpunk kitchen" into Antigravity, and it just... does it. Then you say "add a neon RAMEN sign" and it editsthe same imagewithout re-prompting the whole scene. Oh, and the cover image of this article? Generated by the thing the article is about — dogfooding all the way down. More on that at the end. Most image-generation workflows are stateless . You send a prompt, you get pixels back, and the model immediately forgets everything. Want to tweak the result? You re-describe the entire scene and pray the character, lighting, and composition survive the round trip. Narrator: they don't. Google's NB2Lite — the friendly nickname for gemini-3.1-flash-lite-image — takes a different approach. It's a high-efficiency image model with sub-2-second generations, solid text rendering in 25+ languages, and — the headline feature — support for the stateful Interactions API , which lets you iterate on an image across multiple turns while the model keeps the visual context server-side. This repo glues that capability directly into Google Antigravity CLI , so your coding agent can generate and iteratively refine images as a natural part of a pair-programming session. It ships as two things in one repo: nb2lite-agent , a single-file FastMCP app in server.py exposing four tools. nb2lite-image that teaches Antigravity The Interactions API is Gemini's stateful endpoint. The core loop looks like this: client.interactions.create ... with a prompt and store=True . interaction id previous interaction id , and the model edits the So instead of this stateless suffering : "A watercolor fox in a forest at dawn, mist, soft light, wearing a red scarf, three birch trees on the left, and now alsoholding a lantern" ...you write this in Antigravity: "Add a lantern in its paw." That's it. The stored context holds the rest. A few practical details the server handles for you: 1:1 , 16:9 , 9:16 , 4:3 , 3:4 and low default, fast drafts or high complex rendering, accurate text layout, character composition . The generic API spec also lists minimal and medium , but the live API rejects them for this model with an HTTP 400 — the server saves you from discovering that the hard way.The Model Context Protocol is an open standard for connecting AI assistants to tools and data. Before it, giving a model access to some service meant writing a bespoke integration for each assistant — N assistants × M services, everyone reinventing the same plumbing. MCP collapses that: a tool author writes one MCP server that exposes typed tools, and any MCP-capable client like Antigravity CLI can discover and call them with no per-client glue code. An MCP server is usually a small local process that speaks JSON-RPC over stdio. Antigravity launches it, asks "what tools do you have?", and from then on the model can call them like native functions. The nb2lite-agent server exposes four core tools: | Tool | What it does | |---|---| generate image | Text → 1k image. Saves locally, returns the path + an interaction ID. | edit image | Stateful edit: takes the previous interaction ID + a description of only the change. | edit local image | Uploads any local image file inline base64 and applies an edit — your entry point for existing files. | get help | Reports live config: API key status, active model, output directory, full tool reference. | Images land on disk as gen <timestamp <uuid8 .jpg or edit / edit local prefixed — the UUID suffix keeps concurrent generations from clobbering each other. Errors come back as 🔴 ... text strings rather than protocol errors, so Antigravity can read and react to them gracefully. If MCP is the hands the tools an agent can physically call , a skill is the muscle memory — a markdown file SKILL.md plus bundled resources that load into Antigravity's context and teach it the workflow: which tool to reach for, in what order, with which constraints. For nb2lite-image , the skill encodes things like: get help first when diagnosing setup issues — if the API key is missing, nothing else will work. thinking level: low for drafts.The skill also bundles the MCP server itself mcp/server.py , its requirements, an installer script, and a vendored copy of the Interactions API developer guide — so it's fully self-contained. You need three things: Python 3.10+ , Antigravity CLI , and a Gemini API key free from Google AI Studio https://aistudio.google.com/ . Pick one of the paths below. Inside your Antigravity session, run: /plugin marketplace add xbill9/nb2lite-skill-agy /plugin install nb2lite-image@nb2lite-skill-agy This installs the skill and auto-registers the MCP server. The plugin manifest carries no API key — the server reads GEMINI API KEY from your environment, so make sure it's exported before launching Antigravity CLI. 1. Get the code git clone https://github.com/xbill9/nb2lite-skill-agy.git cd nb2lite-skill-agy 2. One-command setup: installs deps, registers the MCP server in .mcp.json, and prompts for your API key stored in ~/gemini.key ./init.sh 3. Restart Antigravity CLI in this directory and approve the server when prompted. Verify with: /mcp should list nb2lite-agent init.sh is idempotent and safe to rerun anytime. From a clone of the repo: make init TARGET=/path/to/your/project ARGS='--output-dir ./images' This copies the skill into <project /.gemini/antigravity-cli/skills/nb2lite-image/ and writes the nb2lite-agent entry into that project's .mcp.json . It reuses ~/gemini.key if available. Restart Antigravity in your project, approve the server, done. The server is published as xbill9/nb2lite-agent https://hub.docker.com/r/xbill9/nb2lite-agent : antigravity mcp add nb2lite-agent --env GEMINI API KEY="$ cat ~/gemini.key " -- \ docker run --rm -i -e GEMINI API KEY -v "$PWD:$PWD" -w "$PWD" xbill9/nb2lite-agent The -v "$PWD:$PWD" -w "$PWD" mount ensures the container can save images to your workspace disk and read local files for edit local image . /mcp doesn't list the server → restart Antigravity CLI in the project directory. 🔴 GEMINI API KEY is not set → run source set env.sh or export the key and restart. get help ; it reports the live configuration.Once installed, you talk to Antigravity in plain English. A real flow looks like: You: "Generate a cozy cabin in a snowy forest at dusk, 16:9." Antigravity calls: generate image prompt="A cozy log cabin in a snowy forest at dusk, warm light in the windows", aspect ratio="16:9", thinking level="low", 🟢 Saved to: ./gen 1784759001 a1b2c3d4.jpg Interaction ID: v1 ChdpRU5... You: "Nice. Add smoke curling from the chimney." edit image previous interaction id="v1 ChdpRU5...", edit prompt="add gentle smoke curling from the chimney", 🟢 Saved to: ./edit 1784759050 e5f6a7b8.jpg Interaction ID: v1 Xk9mPq2... ← a NEW id; the next edit chains this one You: "Now make it night, with aurora in the sky." Same tool, newest ID, and the cabin, trees, and chimney smoke all stay put — only the sky changes. No re-prompting, no continuity roulette. And for images that didn't come from the model at all: You: "Take ./whiteboard-sketch.png and render it as a clean 3D product mockup." edit local image image path="./whiteboard-sketch.png", edit prompt="render this hand-drawn sketch as a high-fidelity 3D product mockup", aspect ratio="4:3", It returns an interaction ID too — so follow-up refinements switch to edit image and go stateful from there. "Eating your own dog food" means using your own product for real work. It's the difference between "this should work" and "I ship with this every day." This repo dogfoods itself at every layer: nb2lite-image skill and nb2lite-agent server are already wired up, so every development session doubles as an integration test. make test drive the same four MCP tools an end user would, against the live API. generate image prompt="A wide tech blog cover illustration: a friendly AI agent with glowing antigravity elements floating alongside an easel, painting a vibrant galaxy, while a chain of connected frames behind it shows the same picture evolving step by step. Flat vector style, deep indigo background, neon cyan and magenta accents. Title text 'NB2Lite + Antigravity', subtitle 'Stateful image editing as an Antigravity skill'. Crisp, accurate lettering.", aspect ratio="16:9", thinking level="high", 🟢 Image successfully saved • Saved to: /home/xbill/nb2lite-skill-agy/gen 1784832091 d71439ca.jpg • Interaction ID: v1 ChdXbUJpYXB5aEZZYkotOFlQeC1UcG1BNBIXV21CaWFweWhGWWJKLThZUHgtVHBtQTQ That exact output is committed to the repo as devto-cover.jpg https://github.com/xbill9/nb2lite-skill-agy/blob/master/devto-cover.jpg , receipts and all. Worth noticing: thinking level: "high" buys you on text-heavy layouts. edit image with that interaction ID and say "make the cyan accents emerald." That's the whole point.Dogfooding is the cheapest credibility there is: the tool's real output is literally the first thing you saw when you opened this article. This is a third-party community project, not affiliated with or endorsed by Google. Bring your own Gemini API key — and remember generations are billable, so draft on low and save high for final outputs.