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envious-canvas

by saurabhav88
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What is this?

envious-canvas is an MCP server that turns Claude into a full image production studio. Ask Claude to generate images, edit photos, remove backgrounds, upscale resolution, and resize to any platform's specs — all from natural language.

It is not just an API wrapper. It is a workflow tool with brand kits for visual consistency, smart provider routing across 4 AI services, and 69 channel presets so you never have to look up image dimensions again.

What you can do

You say

What happens

"Create a hero image for my blog post about sustainable architecture"

Generates a high-quality image, saves it to your desktop

"Edit this photo to remove the background clutter and make it cleaner"

AI editing that preserves the subject while transforming the scene

"Remove the background from product-shot.png"

Subject isolation with transparent PNG output

"Upscale this image to 4x resolution"

AI-powered super-resolution using Real-ESRGAN

"Resize this for Instagram, LinkedIn, and Facebook"

Creates correctly sized versions for each platform

"Generate a campaign pack for our summer sale"

One prompt produces 10+ channel-ready assets: blog hero, OG image, social cards, display ads

"Create a marketing banner with headline 'Launch Day' and our brand colors"

Renders pixel-perfect HTML/CSS to PNG — precise text, gradients, layouts

Related MCP server: MCP Asset Generator

Quick start

1. Clone and install

git clone https://github.com/saurabhav88/envious-canvas.git
cd envious-canvas
uv sync

Don't have uv? Install it: curl -LsSf https://astral.sh/uv/install.sh | sh

2. Add at least one API key

You only need one provider to get started. Set the key as an environment variable, or place it in a file at ~/.enviouswispr-keys/ (one key per file, e.g. openai-api-key).

Provider

Env variable

What it unlocks

Get a key

OpenAI

OPENAI_API_KEY

Generation + editing (recommended default)

platform.openai.com

Google Gemini

GEMINI_API_KEY

Generation, editing, vision/describe

aistudio.google.com

Replicate

REPLICATE_API_TOKEN

FLUX models, upscale, background removal

replicate.com

fal.ai

FAL_KEY

Fast FLUX generation (fallback)

fal.ai

3. Connect to Claude

Claude Code — add to ~/.claude.json:

{
  "mcpServers": {
    "envious-canvas": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/envious-canvas", "python", "main.py"]
    }
  }
}

Claude Desktop — add to your config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "envious-canvas": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/envious-canvas", "python", "main.py"],
      "env": {
        "OPENAI_API_KEY": "sk-...",
        "GEMINI_API_KEY": "AI..."
      }
    }
  }
}

Replace /path/to/envious-canvas with the actual path where you cloned the repo.

4. Verify it works

# Run smoke tests (no API keys needed)
uv run pytest tests/test_smoke.py -v

That's it. Open Claude and start asking for images. Chromium (for HTML rendering) auto-installs on first server start.

The 6 MCP tools

Tool

What it does

canvas_plan_image

Plan a creative asset — auto-detects fidelity mode, resolves brand kit, estimates cost. Approve before generating.

canvas_create_image

Generate marketing images — plan-based or direct mode. Supports brand kits, multi-channel campaign packs, style control, and provider selection.

canvas_render_layout

Render HTML/CSS templates to pixel-perfect PNG. Supports brand kit CSS variables, Google Fonts, and responsive viewports.

canvas_edit_image

Edit existing images: text-directed edits, remove background, upscale (2-4x), apply color grading.

canvas_manage_library

CRUD for brand kits and generated assets. Create/list/search/delete brand kits, search generated assets.

canvas_inspect_asset

Analyze any image — get descriptions, extract attributes, run QA checks. Powered by Gemini vision.

69 channel presets, 8 platforms

Stop guessing image dimensions. Every preset follows the latest platform specs (sourced from Hootsuite, Sprout Social, Buffer, and HubSpot, 2025-2026).

Platform

Presets

Includes

Facebook

13

Feed (square, landscape, portrait), Stories, cover photos, event/group covers, link preview, ads (square, portrait, landscape, Stories, carousel)

Instagram

11

Feed (square, landscape, portrait, grid), Stories, Reels covers, ads (feed, Stories, Reels, carousel)

LinkedIn

11

Personal/company covers, feed (square, landscape, portrait), article cover, link preview, ads (landscape, square, portrait, carousel)

Twitter/X

7

Header, feed (landscape, square, portrait), summary card, ads (landscape, square)

TikTok

5

Video cover, carousel, ads (vertical, landscape, square)

YouTube

4

Channel banner, video thumbnail, Shorts cover, community post

Pinterest

4

Standard pin, Idea Pin, promoted pin, carousel ad

Google My Business

3

Logo, cover photo, general photo

Web + Google Ads

7

OG image, blog hero (standard + wide), Google Display (landscape, square, portrait), Demand Gen

Plus 4 backward-compatibility aliases for common shorthand (meta_feed_square, linkedin_single, etc.).

Campaign packs

The most powerful feature. One prompt generates a complete, channel-ready asset bundle.

What you say:

"Generate a campaign pack for 'Acme Spring Collection — New Arrivals' using the acme brand kit."

What you get — 10 assets, 10 unique aspect ratios, 8 platforms:

Asset

Aspect

Size

Covers

Square

1:1

1080 x 1080

Facebook, Instagram, TikTok, Google Display

Portrait

4:5

1080 x 1350

Facebook, Instagram, LinkedIn, Google Display

Instagram grid

3:4

1080 x 1440

Instagram profile grid

Pinterest pin

2:3

1000 x 1500

Pinterest

Stories/vertical

9:16

1080 x 1920

Instagram/Facebook Stories, TikTok, Google Demand Gen

YouTube banner

16:9

2560 x 1440

YouTube channel art, blog hero, video thumbnail

OG image

1.91:1

1200 x 630

Link previews, Google Display landscape

Wide banner

2:1

1600 x 800

Email headers, wide banners

X/Twitter header

3:1

1500 x 500

X/Twitter banner

LinkedIn cover

4:1

1584 x 396

LinkedIn personal banner

All from a single generation, cropped to each channel's exact spec. Saved to your desktop, ready to use.

Brand kits

Keep every image on brand, across every provider and channel.

name: acme
color_palette: ["#1a1a2e", "#e94560", "#f5f5f5"]
typography: ["Inter", "Playfair Display"]
visual_vibe: "modern minimalist, warm, approachable"
positive_prompt: "clean, professional, modern design"
negative_prompt: "cluttered, busy, text, watermark, low quality"
safe_use_rules: ["no people", "no text in image"]
preferred_aspects: ["16:9", "1:1", "4:5"]

Create one:

"Create a brand kit called 'acme' with deep navy and coral red colors, modern minimalist style, and a rule to never include text in images."

Use it everywhere:

"Generate a blog hero for our product launch using the acme brand kit."

Brand kits are saved as YAML files and persist across sessions.

Configuration

Variable

Default

Description

OPENAI_API_KEY

—

OpenAI API key

GEMINI_API_KEY

—

Google Gemini API key

REPLICATE_API_TOKEN

—

Replicate API token

FAL_KEY

—

fal.ai API key

IMAGE_GEN_OUTPUT_DIR

~/Desktop/envious-canvas

Where generated images are saved

IMAGE_GEN_BRAND_KITS_DIR

./brand-kits

Where brand kit YAML files live

Known limitations

  • OpenAI size constraints — gpt-image-1 only supports 1024x1024, 1536x1024, and 1024x1536. Other sizes fall back to 1024x1024. Use export_asset_preset to resize after generation.

  • Campaign pack cropping — The hero image is center-cropped to each channel spec. Works well for abstract/gradient art; may clip subjects in photographic content. Per-channel regeneration is planned.

  • Gemini model churn — Google frequently deprecates preview models. If you get 404 errors, check Google's model list for current model IDs.

  • fal.ai is fallback-only — fal activates only when OpenAI and Gemini are both unavailable.

Architecture

envious-canvas/
├── main.py                # FastMCP server entry point
├── providers/
│   ├── base.py            # Canonical types, Provider protocol, Capability enum
│   ├── registry.py        # Tier-based model catalog (27 models, 11 tiers)
│   ├── router.py          # Capability-validated provider routing
│   ├── renderer.py        # HTML-to-image rendering via Playwright
│   ├── openai.py          # OpenAI provider (GPT Image 1/1.5 + DALL-E)
│   ├── gemini.py          # Google provider (Gemini Image + Imagen 4)
│   ├── replicate.py       # Replicate provider (FLUX models, Real-ESRGAN, RemBG)
│   └── fal.py             # fal.ai provider (FLUX models)
├── brand/
│   └── kit.py             # Brand kit CRUD + prompt transformation
├── presets/
│   └── channels.py        # 69 channel export specs across 8 platforms
├── brand-kits/            # YAML brand kit storage (persists across sessions)
├── tests/
│   ├── test_smoke.py      # 22 smoke tests (no API keys needed)
│   └── eval.xml           # 10 MCP evaluation questions
├── pyproject.toml
└── README.md

Provider routing

Each provider declares its capabilities. When you request an action, the router:

  1. Matches the requested capability to available providers

  2. Validates the request is compatible (you cannot send an upscale request to OpenAI)

  3. Selects the best provider based on tier priority and availability

  4. Falls back gracefully if the primary provider is unavailable

Model catalog — 27 models across 11 tiers

Tier

Purpose

Models

fast_generate

Cheapest/fastest

GPT Image 1 Mini, Gemini Flash, Imagen 4 Fast, FLUX Schnell

quality_generate

Best balance

GPT Image 1, Imagen 4, FLUX.2 Pro, FLUX.2 Flex (fal)

premium_generate

Highest fidelity

GPT Image 1.5, Imagen 4 Ultra, FLUX.2 Flex, FLUX.2 Max (fal)

edit

Standard editing

GPT Image 1, Gemini Flash, Kontext Pro

premium_edit

Best editing

GPT Image 1.5, Kontext Max

fill_expand

Inpainting/outpainting

FLUX Fill Pro

describe

Vision/analysis

Gemini Flash (Vision)

upscale

Resolution enhancement

Real-ESRGAN (2x-4x)

remove_background

Subject isolation

RemBG

brand_finetuned

Brand-locked generation

FLUX Pro Finetuned

legacy

Backward compatibility

DALL-E 3, DALL-E 2

Testing

# Smoke tests — no API keys, no network calls
uv run pytest tests/test_smoke.py -v

# Verify server starts and responds to MCP handshake
echo '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"0.1"}}}' | uv run python main.py

Requirements

  • Python 3.11+

  • uv package manager

  • At least one API key (OpenAI, Gemini, Replicate, or fal.ai)

  • Claude Code or Claude Desktop

License

MIT

Install Server
A
license - permissive license
A
quality
D
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

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