Image Gen Pro MCP

Give your AI assistant real image generation, and get back files you can actually use.

Ask Claude to build you a landing page and you'll get a lovely layout with hero-image.png referenced everywhere and no hero image anywhere. This fixes that. Your assistant describes the picture it needs, this makes it, and it hands back a file path that an <img src> can point at.

You need at least one API key. Setup takes about five minutes.

{
  "mcpServers": {
    "image-gen-pro": {
      "command": "npx",
      "args": ["-y", "image-gen-pro-mcp"],
      "env": {
        "OPENAI_API_KEY": "paste-your-key-here",
        "IMAGE_GEN_OUTPUT_DIR": "/absolute/path/to/your/project/public/images"
      }
    }
  }
}

What you get back

Paths, not pictures.

Generated 2 image(s) with **Gemini 3.1 Flash Image** via the direct route.

- `/Users/you/site/public/images/hero-4be44f.jpg` (332 KB, image/jpeg)
- `/Users/you/site/public/images/hero-40cab6-2.jpg` (318 KB, image/jpeg)

_Used 1 reference image._

_58 image(s) left this hour._

That's deliberate, and it's the main design decision in here. A 1024x1024 image is roughly 1.4 MB once it's base64'd into a chat. Three of those is a chunk of the conversation spent on bytes your assistant can't look at anyway. You wanted a file; here's the file.

Related MCP server: DALL-E MCP Server

The models

One tool, six models, because the right one genuinely differs by job.

"nanobanana" is in there because that's what people call Gemini's image model out loud. A server that only accepts the official string makes you look up a name you already know.

Real SVGs

Set svg: true and you get vector source from Arrow 1.1: actual <svg> markup built from shapes and geometry, which you can open, edit and scale. The raster models can't do this. Ask any of them for an SVG and you get a picture that looks like one, which is a different thing and useless if you wanted to change the colour of one path.

After an SVG, the tool suggests offering you a raster version too, in a similar flat style, from one of the picture models. It won't generate that unasked; it's a second billed call.

Seven models, six hard prompts

One prompt per category, shown in full below each heading, the same text sent to all seven, one generation each, nothing cherry-picked and nothing retried. The prompts are deliberately awkward: each one states a rule you can check by counting or looking, so you can grade the results yourself instead of taking my word for which is prettier.

Run 30 July 2026. These are single samples, not a benchmark. Re-run any of them and you may get a different answer.

1. Exact count and arrangement

Exactly seven identical brass hexagonal bolts on a plain grey surface, arranged as two rows of three with the seventh bolt standing upright on its end at the front centre. No bolt touches another.

GPT Image 2 and Gemini 3 Pro are the clean passes. Recraft produced by far the best photograph of the six and got the count wrong, which is the trade in one image. Arrow counted correctly (it emits seven shapes, so the count is in the source) and ignored the layout.

2. Absence

An empty hotel corridor at 3am. No people, no reflections in any mirror, no light under any door, nothing on the floor. Bare carpet, closed doors, one ceiling light.

Text encoders have no NOT operator, so "no mirrors" tends to summon mirrors. GPT Image 2 came back fully compliant: nobody there, nothing on the floor, no light under any door, one fitting. Seedream put two framed mirrors on the walls and two ceiling lights, and stamped "AI generated" across the corner.

3. Attribute binding against the obvious pairing

Five objects in a row on a white studio backdrop: a matte black ceramic banana, a glossy yellow steel wrench, a translucent green wooden mug, a brushed copper cloth apple, a rough concrete glass fish.

Every colour is deliberately wrong for its object, so a yellow banana is a visible failure rather than a matter of taste. GPT Image 2 bound all five and made a decent fist of the materials. Grok bound all five too, at a third of the time, then melted the wrench and drew the fish as a wireframe.

4. Geometric constraint

A clean flowchart: three rectangular boxes in a horizontal row connected left to right by two straight arrows. Every line at exactly 0 or 90 degrees, uniform gaps between boxes, no crossing lines, thin uniform stroke weight, flat colour, no shading.

This is the category the vector model exists for, and it wins it: exact geometry, uniform stroke, labelled boxes, and you can open the file and edit any of it. Recraft came second and labelled its boxes too, with uneven widths. GPT Image 2 drew clean unlabelled boxes of three different sizes. Zoom in on any of the raster ones and the "straight" lines wobble, because a sampler is hallucinating straightness pixel by pixel rather than drawing a line.

5. Reflection and shadow

A chrome kettle on a mirrored tabletop beside a window at 4pm. The reflection in the tabletop shows the underside of the kettle, and its shadow falls away from the window across the table.

Reflections are the reliable giveaway of a model compositing plausible patches rather than simulating a scene. Recraft's is the photograph you would actually ship, and its reflection is a mirror-flip of the kettle's side rather than the underside the brief asked for. Arrow, which cannot photograph anything, got the structure right: reflection present, shadow falling away from the window, light patch on the table.

6. A person, with the pose specified

A woman in her fifties sitting on a wooden stool against a bare plaster wall, three-quarter view facing left, wearing a plain charcoal roll-neck jumper. Her hands rest one on top of the other on her left knee, with exactly five thin silver rings between them. Single hard window light from the right, casting her shadow onto the wall to the left. Sharp focus on the eyes, shallow depth of field, 85mm.

Four checkable things: which way she faces, whether one hand sits on the other, how many rings, and which side the shadow falls. Gemini 3.1 Flash lit her from the left and threw the shadow right, which is the one unambiguous miss in the whole run. Gemini 3 Pro, on the identical prompt, got it right, which is the clearest reason to reach for the slower sibling. Recraft produced the photograph you would ship and turned three-quarter into near-profile. Arrow, which cannot photograph anyone, followed the pose most closely of the six.

What this suggests

Nothing here is a benchmark, but the shape is consistent enough to route by:

• GPT Image 2 for briefs with rules in them. It was the only model to pass the count outright and the only one to honour every "no" in the absence prompt.

• Recraft V4.1 Pro when it has to look like a photograph, and check the details yourself.

• Arrow 1.1 for anything diagrammatic, where it is not competing with the others so much as doing a different job.

• Gemini 3.1 Flash as the cheap default, with the caveat that it is the one model observed to invert a stated light direction.

• Gemini 3 Pro Image when Flash gets something wrong. Same key, same route, roughly twice the wait; it passed the count and fixed Flash's lighting miss.

• Grok Imagine when you want six options in the time another model takes for one.

• Seedream 5.0 Pro last on this evidence: slowest by a wide margin, and it watermarks.

I inspected 22 of these 42 images closely; the notes above cover those and the rest are here for you to judge. Every file is in docs/examples/.

Writing a prompt that works

Front-load the subject, then action or pose, setting, style, composition, lighting, key details. Natural prose rather than keyword tags, two to five sentences. Say what to include rather than what to leave out. One scene per prompt.

Two things worth knowing before you reach for this at all:

If it needs exact text, real numbers, or a correct structure, build it in code. Charts from data, labelled diagrams, tables, UI mockups, multi-panel grids. Image models garble words, invent numbers, and draw bars that match no data, and re-prompting garbles them again. HTML and CSS give you exact values and a layout you control; screenshot that. Use this server for the parts where only the look matters.

Nothing here remembers a subject between calls. Two generations of "the same" character always drift. Generate one canonical image, then pass its path in referenceImages on every later call and restate the fixed traits each time.

The tool result tells you both of these when the prompt looks like it needs them, along with which model handled that kind of brief best in the comparison above.

When a model says no

Providers refuse things. When one does, the result explains what to do instead rather than just reporting the error, and it tells the calling agent not to reword its way past the filter or shop the prompt around other models.

The suggestions only move in one direction: from naming an entity toward describing an aesthetic. "In Pixar style" is usually not a request for that studio at all, it is a request for stylised 3D animation, and naming the properties gets a better image than naming the studio would have:

stylised 3D character animation, oversized eyes with visible catchlights, soft subsurface scattering on the skin, rounded simplified forms, warm key light with cool bounce, shallow depth of field

Same for a borrowed character: describe the build, costume shapes, silhouette and expression you actually want, and the result is reusable as a referenceImages anchor, which a trademark never is.

Where a request has no legitimate version, it says so and offers nothing. That distinction is the whole point of the feature.

Reference images and context

Two things beyond the prompt, and both change the result.

Reference images. Paths to files on your machine. Use them to match an existing style, keep a product or character consistent across a set, or edit an image you already have.

Context. Extra text kept separate from the prompt: brand rules, a palette, what the surrounding page says, what to avoid. Separate so the subject stays legible instead of drowning in instructions.

prompt:          "Hero illustration: a paper aeroplane over a pale gradient"
referenceImages: ["/Users/you/brand/style-sheet.png"]
context:         "Match the brand palette in the reference. Flat vector, no gradients on the subject, no text anywhere in the image."
n:               3

Note: reference images are read from disk by this server, so absolute paths are safest, and they must be readable by whatever runs the MCP. They're not URLs and not base64.

Rough edges, said plainly

GPT Image 2 needs project access your OpenAI account might not have. Mine doesn't. The error is clear when it happens (Project ... does not have access to model gpt-image-2), but it's the default model, so if you hit it on your first try, that's why. Check your project's model permissions in the OpenAI dashboard, or just use gemini instead.

Gemini can't do more than one image per call. Asking for three means three requests behind the scenes, fanned out in parallel. Worth knowing why three Gemini images take about as long as one and cost three times as much.

Nobody agrees what format they're returning. Gemini returns JPEG, Recraft returns WebP, and Arrow returns SVG source labelled image/png. The server sniffs the actual bytes and names the file accordingly rather than trusting the label, so you get .jpg, .webp and .svg where those are what arrived. Nothing for you to do; it's just why extensions vary.

Recraft goes through a different endpoint. The gateway's own image-model route answers with an error for every Recraft model, so this uses its OpenAI-compatible endpoint instead, which serves them correctly. Checked 30 July 2026.

Getting a key

You need at least one. Any one is enough to start.

OpenAI (platform.openai.com/api-keys) gives you GPT Image 2, subject to the access caveat above.

Google AI Studio (aistudio.google.com/apikey) gives you Gemini 3.1 Flash Image. Sign in, click Create API key, copy it. This is the quickest one to get working.

Or no key at all, for one route: if you have xAI's grok CLI installed and signed in, style: "grok-cli" uses that session and bills your X Premium or SuperGrok subscription rather than any API balance. It reads the token from ~/.grok/auth.json, never writes to it, and never refreshes it, because two programs writing one auth file is how a working login gets broken.

Vercel AI Gateway (vercel.com/docs/ai-gateway) gives you everything from one key, and it's the only way to reach Recraft, Seedream, Grok Imagine and Arrow, none of which have a first-party key here. If you only get one key, get this one.

Treat any of them like a password, because that's what they are. Don't paste one into a chat, don't commit one to a repo.

About money, and a plain warning

Every image costs you. Image models are billed per image and they're the expensive kind, meaningfully more than a text call. Prices move, so check each vendor's current pricing rather than trusting a number written here.

Set a budget before you start. OpenAI, Google Cloud and Vercel all let you set a spending limit and an alert. It takes two minutes each and it's the difference between a surprise and a number you chose. If you only do one thing from this section, do that one.

This server ships with an hourly cap of 60 images, on by default, because an assistant stuck in a retry loop is exactly the thing that runs up a bill nobody agreed to. There's also a per-call cap of 4, so "generate some options" can't quietly become forty. Both are yours to raise, deliberately rather than by accident.

No liability for spend. This is free software provided as-is under the MIT licence. You are responsible for your own API usage and any charges OpenAI, Google or Vercel bill you, including charges caused by bugs, misconfiguration, runaway agents, or anything else. Neither Luke Rhodes nor Fledgeling is liable for your spending. Set a budget.

Settings

All optional except having at least one key. Set them alongside the key in the env block.

Two things the server handles that are worth knowing only if you're reading the code. The same model has different ids per route (gemini-3.1-flash-image direct, google/gemini-3.1-flash-image on the gateway). And Gemini's image models are multimodal language models, so they're reached through the text API, not the image one; the gateway types them language and refuses them on the image route.

What it doesn't do

Worth saying plainly, so you know before you install it.

It doesn't return image data to your assistant, so your assistant can't look at what it made. It knows the file exists and where; it can't see whether the composition worked. Open the file yourself, or hand the path to a tool that can read images.

It doesn't upscale, crop, convert, or optimise. One job.

It won't rescue a vague prompt. Reference images help more than adjectives do.

For developers

TypeScript, ESM, Node 20.11 or newer. FastMCP over stdio, Vercel AI SDK v7 (generateImage, not the removed experimental_generateImage), Zod at every boundary, exactOptionalPropertyTypes on, no any.

npm install
npm run gate     # typecheck, lint, test, build

Routing lives in src/models.ts as a table and a pure function, because routing is the part people get wrong and a chain of ifs spread through a handler is how it stays wrong.

Who made this

I'm Luke Rhodes, a founder and engineer building Fledgeling. I spent years shipping developer tools and got tired of proprietary formats and slow editors, so Fledgeling is the opposite of that: fast software built from scratch, your work in plain text you own, and AI as a collaborator you can always overrule. The human is always the editor of record.

I'm also co-founder of Diolog with Amy Benson, building investor-relations software for listed companies and the retail investors following them.

• GitHub: github.com/lprhodes

• LinkedIn: linkedin.com/in/lukerhodes

• X: x.com/lp_rhodes

• Email: hello@fledgeling.app

Three related things

Google Search MCP is this one's sibling. Where this makes pictures, that one does the looking: Google search through the Gemini API, returning the answer, the real source URLs (not Google's opaque redirects), and the queries the model actually ran.

npx -y google-search-api-mcp

Dossier is the big one. It runs proper deep research: several backends on the same question at once (Gemini, Perplexity, OpenAI, xAI, plus any coding CLIs you're already paying for), then checks the citations resolve, flags where the backends disagree, and refuses to spend past a budget you set.

npx -y dossier-research-mcp

Margin is for teams rather than terminals. It turns internal reference docs into living pages you can comment on by typing or talking, listen to, and edit in place, with an embedded agent who rewrites sections from your feedback and commits the change to git. Members-only and invite-gated at the moment, with a waitlist.

Licence

MIT. Use it, fork it, ship it.