cascade-img

Name: cascade-img
Author: laffeyp

by laffeypversion 0.1.1i★ –iRuns on your computer

Registry nameio.github.laffeyp/cascade-img

Install

Overview

Description from the maker (original English)

LLM-operable Midjourney pipeline: 21 MCP tools to compose, generate, curate with vision, and log.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory
You need to fill in: DISCORD_USER_TOKEN, MJ_CHANNEL_ID, MJ_IMAGINE_VERSION

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: DISCORD_USER_TOKEN, MJ_CHANNEL_ID, MJ_IMAGINE_VERSION, CASCADE_BRIDGE_URL

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "cascade-img": {
      "command": "uvx",
      "args": [
        "cascade-img"
      ],
      "env": {
        "DISCORD_USER_TOKEN": "your-value",
        "MJ_CHANNEL_ID": "your-value",
        "MJ_IMAGINE_VERSION": "your-value",
        "CASCADE_BRIDGE_URL": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add cascade-img --env DISCORD_USER_TOKEN=your-value --env MJ_CHANNEL_ID=your-value --env MJ_IMAGINE_VERSION=your-value --env CASCADE_BRIDGE_URL=your-value -- uvx cascade-img

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "cascade-img": {
      "command": "uvx",
      "args": [
        "cascade-img"
      ],
      "env": {
        "DISCORD_USER_TOKEN": "your-value",
        "MJ_CHANNEL_ID": "your-value",
        "MJ_IMAGINE_VERSION": "your-value",
        "CASCADE_BRIDGE_URL": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "cascade-img": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "cascade-img"
      ],
      "env": {
        "DISCORD_USER_TOKEN": "your-value",
        "MJ_CHANNEL_ID": "your-value",
        "MJ_IMAGINE_VERSION": "your-value",
        "CASCADE_BRIDGE_URL": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

DISCORD_USER_TOKEN secret required env
Discord user-account token the bridge daemon uses to talk to Midjourney (captured from the Discord desktop app; see RUNBOOK.md).
MJ_CHANNEL_ID required env
Discord channel ID where the Midjourney bot runs.
MJ_IMAGINE_VERSION required env
Version token for Midjourney's /imagine slash command, captured from Discord.
CASCADE_BRIDGE_URL env
URL of the running cascade-mj-bridge daemon that cascade-mcp forwards to.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account laffeyp, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7286 servers in this category
Published to the registry more recently than 22% of the other 7286 servers in this category
Basic checks equal to or better than 100% of the other 7286 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is cascade-img safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: DISCORD_USER_TOKEN, MJ_CHANNEL_ID, MJ_IMAGINE_VERSION

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.1) reached the registry 4 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

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Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Generate Midjourney images by conversation instead of by hand. You describe what you want; your AI assistant composes the prompt, fires it, inspects the grid with vision, crops the best quadrant, cleans it up, and logs what worked.

You: "I need a flat-design mountain icon, centered, simple shapes, transparent background"

Agent: reads prompt log → composes prompt from parts → fires imagine → waits → inspects 2x2 grid with vision → picks best quadrant → crops it → removes background → saves → logs what worked

cascade-img is an MCP server with 23 tools that plugs into Claude, Cursor, Codex, or anything that speaks MCP. Midjourney is the first backend; Flux, DALL-E, and Imagen are on the roadmap. There's also a CLI.

Not a programmer? Open an AI assistant that can run commands (Claude Code, Cursor, or Cline), point it at this repo, and say: *"Read RUNBOOK.md and set up cascade-img on this machine, then let me make images by describing them to you."* It does the technical parts. You just need a Midjourney subscription and to copy a few values from Discord.

Quick Start

You need: a paid Midjourney subscription, a Discord account with the MJ bot in a channel, and Python 3.12 or newer.

pip install cascade-img

Or from source:

git clone https://github.com/laffeyp/cascade-img

cd cascade-img/packages/python pip install -e .

This puts three commands for operating cascade-img on your PATH: cascade-mj-bridge (the daemon), cascade-mcp (the MCP server), and cascade-mj (the CLI). Installing also adds a fourth command, cascade-trace-check — a diagnostics validator (not part of the generation loop) that replays a recorded event log and checks it against the vocabulary's declared event ordering and timing rules.

Configure — you need four values from the Discord desktop app (channel ID, server ID, imagine version, and your user token). Takes about five minutes. RUNBOOK.md walks through each one step by step.

cp "$(python -c 'import cascade_img, pathlib; print(pathlib.Path(cascade_img.__path__[0]) / ".env.example")')" .env

Fill in the four values per RUNBOOK.md, then validate:

cascade-mj-bridge --check-env --pretty

Start the daemon in one terminal, then connect from another:

cascade-mj-bridge          # leave running — holds the Discord connection

Connect your AI assistant — add to your MCP config (Claude Desktop, Cursor, Cline):

"mcpServers": { "cascade-img": { "command": "cascade-mcp" } } }

Or point your assistant at this repo and ask it to read AGENTS.md — it'll wire everything up.

Or use the CLI:

echo '{

"mountain-icon": { "subject": "a flat-design icon of a mountain, centered, simple shapes", "aspect_ratio": "1:1" } }' > assets.json

cascade-mj mountain-icon --registry assets.json --upscale all --pretty

The 23 Tools

Category	Tools	What they do
Onboarding	`cascade_guide`	Returns the full operating manual in one call — the loop, every tool, the failure→action table. Call it first; the generation and curation tools are gated until it's read.
Generation	`imagine`, `generate_video`, `wait`, `status`, `bridge_health`, `mj_action`	Compose and fire prompts, poll for results, check daemon health, trigger Midjourney actions (upscale, vary, pan)
Catch-up	`channel_recent`, `adopt_message`	See what the human did by hand in Discord and claim those results into the pipeline — adopted messages become normal jobs that curation and `mj_action` work on
Composition	`compose_prompt`, `compose_video`	Build prompts from structured parts — subject, moodboard, style refs, aspect ratio, negatives — not freeform text
Curation	`crop_grid`, `alpha_key`, `auto_trim`, `palette_quantize`, `contact_sheet`, `sprite_sheet`, `score_grid`, `video_filmstrip`, `loop_seam_delta`, `promote`	Extract quadrants from grids, remove backgrounds, trim whitespace, build sprite sheets, score results with vision, promote winners to final output
Working memory	`log_append`, `read_prompt_log`	Append-only prompt log the agent reads before every run — what was tried, what worked, what didn't. Persists across sessions.

Every call returns {ok, result} or {ok: false, error: {code, remediation}}. Branch on the stable code, not the message. Full tool reference in AGENTS.md.

How This Differs

Other open-source Midjourney tools focus on the generation step — fire the prompt, hand back the image. cascade-img does the work around that:

Vision-based self-curation — the agent inspects its own output and picks the best quadrant
Structured prompt composition — prompts built from parts (subject, style, identity, constraints), not raw strings
Working memory — append-only log persists across sessions; each run reads what came before
Curation pipeline — crop grids, remove backgrounds, build sprite sheets, promote winners
MCP-native — 23 tools that plug into Claude, Cursor, Codex, or anything that speaks MCP
Pluggable backends — Midjourney now, Flux/DALL-E/Imagen on the roadmap

How It Works

One daemon, two stateless clients, all over local HTTP:

`cascade-mj-bridge` — the daemon. Only process that talks to Discord. Holds the live connection and tracks in-flight jobs. Must stay running.
`cascade-mcp` — the MCP server. Stdio by default (Claude Desktop / Cursor / Cline); --http for HTTP. Stateless — start and stop freely.
`cascade-mj` — the CLI. Takes an asset ID and a registry, composes the prompt, fires, waits, writes to the log.

Prompts are composed from structured parts, not written as raw strings:

from cascade_img.prompt.composer import PromptComposer, Subject, StyleStack, IdentityStack

prompt = PromptComposer().compose( Subject( text="a flat-design icon of a mountain", constraints=["centered", "simple shapes", "transparent background"], ),

Both optional. moodboard is a Midjourney personalization code;

sref/oref are reference-image URLs you'd set up in MJ first.

style=StyleStack(moodboard="abc123def", sref="https://cdn.example.com/style.png"), identity=IdentityStack(oref="https://cdn.example.com/ref.png", ow=1000), aspect_ratio="1:1", version="7", )

All three entry points emit structured JSON and follow the same {ok, result | error: {code, remediation}} envelope. Every failure carries a stable error code (e.g. DISCORD_401, MJ_UUID_MISSING, UPSCALE_BUTTON_FAILED) with a machine-readable remediation — so a caller branches on the code, not the message. The full catalog of log events and error codes is in vocabulary/0.1.json, and a trace checker (cascade-trace-check) enforces event ordering over recorded runs.

Midjourney terminology

prompt — the text + flags you send Midjourney. grid — the 2x2 set of four candidates returned per prompt. quadrant / U1-U4 — the four cells; "U2" means upscale the second. upscale — render one cell at full resolution. aspect ratio (`--ar`) — output shape. sref — an image whose *style* to borrow. oref — an image whose *subject identity* to keep across poses. moodboard (`--p`) — a saved personalization profile. stylize (`--s`) — how strongly MJ applies its own aesthetic.

Documentation

Doc	What it covers
AGENTS.md	The LLM operator's guide. Read this when handing cascade-img to an agent.
RUNBOOK.md	Install, env capture, setup, reconnect lifecycle, every failure mode with error codes and fixes.
CAPABILITIES.md	Every Midjourney feature cascade-img drives — prompt parameters, mj_actions, the V8.1/V7 split.
ARCHITECTURE.md	Internal architecture and design decisions.
examples/	Three walkthroughs: single image, batch, video. Read AGENTS.md first.
CHANGELOG.md	Release history.

Roadmap

Version	What's in it
v0.1 (current)	MJ backend (V8.1 + V7), prompt composer, curation tools, MCP server, CLI, prompt log
v0.2	Channel catch-up + message adoption — landed on main: `channel_recent` and `adopt_message` let the agent see what the human did by hand in Discord and act on it (design); still to come: more MJ commands (`/describe`, `/blend`, Vary Region inpaint, `/tune`), retro-U-press on adopted grids, internal refactoring
v0.3	TypeScript wrapper; first API backends — Flux via Fal + Flux Kontext, Ideogram
v0.4	Google Imagen, Recraft (native vector/SVG)
v0.5	OpenAI gpt-image, Stable Diffusion
v1.0	API stable, three+ backends in production

Every backend implements one interface, so a later release can chain them — generate on one provider, refine on a second (e.g. Flux Kontext), upscale on a third.

Repository Layout

cascade-img/

├── packages/python/ # the Python package (cascade_img) │ ├── src/cascade_img/ # prompt/, interfaces/, backends/, curation/, vocabulary/ │ ├── tests/ # behavior tests │ └── tools/ # live smoke walk ├── examples/ # three walkthroughs of the operating loop ├── vocabulary/0.1.json # event log-line catalog └── *.md # README, ARCHITECTURE, RUNBOOK, AGENTS, CAPABILITIES, ...

Disclaimer

This tool automates Midjourney through a Discord user account. A paid Midjourney subscription is required. Both Discord and Midjourney's Terms of Service prohibit user-account automation. This is the same mechanism used by every open-source MJ tool (midjourney-proxy, midjourney-api, etc.) — there is no public Midjourney API. Use at your own risk.

The backend interface is pluggable — Flux, DALL-E, and Imagen are on the roadmap.

License

Apache-2.0. See LICENSE.

cascade-img

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

Quick Start

Fill in the four values per RUNBOOK.md, then validate:

The 23 Tools

How This Differs

How It Works

Both optional. moodboard is a Midjourney personalization code;

sref/oref are reference-image URLs you'd set up in MJ first.

Documentation

Roadmap

Repository Layout

Disclaimer

License