AI Carousel Generator

Generate premium, viral-ready 4K carousel slides from a single instruction - an expert copywriter, an AI image engine, and a premium compositor, all exposed as native MCP (Model Context Protocol) tools so any AI client can call them.

• 4K output - native 2160x2700 px (4:5 portrait) composites
• High-EEAT copywriting - data-backed insights, actionable frameworks, zero fluff, LinkedIn-optimized hooks (per-slide visual_prompt, headline, body, animation_cue)
• Premium typography - Notion-meets-Bloomberg layout: glassmorphic text cards, editorial type scale, slide indicators, animation-cue tags
• Master tool - one instruction in, a packaged set of slides + an animation script (timings) out

Architecture

AI client (opencode, Claude Code, Claude Desktop, ...)
        |
        |  MCP stdio transport
        v
  mcp_server.py  (FastMCP "AI Carousel Generator")
        |
        +--> services/copywriter.py    expert content strategy (slide plan)
        +--> services/ai_generator.py  AI image engine (4K backgrounds)
        +--> services/compositing.py   premium typography compositor

The three MCP tools:

Tool	Purpose
create_single_slide	Generate one AI background + compose one slide
batch_generate_carousel	Turn a high-level topic into a full carousel
create_premium_viral_carousel	Master tool: copywriting + 4K visuals + packaged animation metadata

Where does the "antigravity CLI" fit?

The antigravity CLI is not an MCP client - it is the image-generation backend that mcp_server.py shells out to. The flow is:

AI client --MCP--> mcp_server.py --subprocess--> antigravity CLI -> 4K background PNG
                                                   (or a custom local HTTP endpoint)

So you do not "connect the MCP server to antigravity CLI". Instead you connect your AI client to this MCP server, and the server renders images through antigravity (or your own endpoint) for you. Configure the backend with environment variables (see Environment).

Requirements

• Python 3.10+
• uv (recommended, portable) - or an existing virtual environment (pip install -r requirements.txt)
• The antigravity CLI on PATH, or a local image endpoint (GENERATION_PROVIDER=endpoint), or the optional placeholder fallback

Quick start

pip install -r requirements.txt        # or: uv sync / uv venv
python mcp_server.py                    # runs over stdio - expected for MCP clients

Smoke-test the tools directly:

python _mcp_smoke.py

Connecting an MCP client

The server speaks stdio, the transport used by Claude Code, Claude Desktop, and opencode. No port, no web server.

opencode

Project-level (works only in this folder) - commit this to the repo:

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "ai-carousel-generator": {
      "type": "local",
      "command": [".venv/Scripts/python.exe", "mcp_server.py"],
      "enabled": true
    }
  }
}

Global (available from any folder) - ~/.config/opencode/opencode.json:

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "ai-carousel-generator": {
      "type": "local",
      "command": [
        "C:\\mcp-cursoal\\.venv\\Scripts\\python.exe",
        "C:\\mcp-cursoal\\mcp_server.py"
      ],
      "enabled": true,
      "environment": {
        "IMAGE_OUTPUT_DIR": "C:\\mcp-cursoal\\generated_images",
        "COMPOSED_SLIDES_DIR": "C:\\mcp-cursoal\\composed_slides"
      }
    }
  }
}

On machines with uv (instead of a local .venv), the portable variant is:

{
  "mcp": {
    "ai-carousel-generator": {
      "type": "local",
      "command": [
        "uv", "run", "--directory", "C:\\mcp-cursoal",
        "--with-requirements", "requirements.txt",
        "python", "mcp_server.py"
      ],
      "enabled": true
    }
  }
}

After changing config, quit and restart opencode - config is loaded once at startup. Then ask: "Generate a 5-slide 4K carousel about B2B lead generation".

Claude Code

Register once (no file editing needed):

# uv-based (portable)
claude mcp add ai-carousel-generator --scope user -- \
    uv run --directory C:\mcp-cursoal --with-requirements requirements.txt \
    python mcp_server.py

# or with the local venv
claude mcp add ai-carousel-generator --scope user -- \
    C:\mcp-cursoal\.venv\Scripts\python.exe mcp_server.py

Verify with claude mcp list. Restart Claude Code, then prompt it to create a carousel.

Claude Desktop

Edit claude_desktop_config.json (Claude -> Settings -> Developer -> Edit Config):

{
  "mcpServers": {
    "ai-carousel-generator": {
      "command": "uv",
      "args": [
        "run", "--directory", "C:\\mcp-cursoal",
        "--with-requirements", "requirements.txt",
        "python", "mcp_server.py"
      ]
    }
  }
}

Restart Claude Desktop, then look for the hammer/tools icon next to the carousel generator server.

Tools

create_premium_viral_carousel(user_instruction, slide_count=3, render_resolution="4K") -> dict

Master tool. Orchestration: copywriter -> 4K image generator -> compositor.

• user_instruction - high-level brief, e.g. "B2B lead generation in 2026"
• slide_count - number of slides (>= 1)
• render_resolution - "4K" (2160x2700) or "HD" (1080x1350)

Returns a dict with topic, slide_count, render_resolution, output_dir, slides (per-slide slide_number, headline, body, animation_cue, image_path) and metadata.animation_script (per-slide cue, duration_ms, start_ms for Canva / After Effects).

Example instruction: "Morning routines for high performers" -> hook slide, data slide, framework slide, CTA slide - each a 2160x2700 PNG in composed_slides/.

batch_generate_carousel(topic, slide_count) -> list[str]

Fast path: archetype-driven breakdown (Why It Matters, The Basics, Key Principle, ...) returning a list of slide file paths.

create_single_slide(topic_prompt, headline, body, slide_number) -> str

Fine-grained control: one background prompt + text + position, returns the final slide path.

Environment

All variables are optional (defaults shown):

Variable	Default	Description
GENERATION_PROVIDER	antigravity	antigravity or endpoint
ANTIGRAVITY_CLI	antigravity	Path to the antigravity CLI executable
ANTIGRAVITY_MODEL	gemini-2.5-flash-image	Image model for antigravity
ANTIGRAVITY_ASPECT_RATIO	4:5	Portrait 4K carousel aspect ratio
TARGET_RESOLUTION_LABEL	4K	Label for the target resolution
GENERATION_API_URL	http://localhost:8001/generate	Custom endpoint URL
GENERATION_TIMEOUT_SECONDS	120	Per-generation timeout
DEFAULT_IMAGE_STYLE	(empty)	Style hint for every generation
AI_GENERATION_FALLBACK	false	true = render placeholder PNGs if the engine is unavailable
IMAGE_OUTPUT_DIR	generated_images	Where raw AI backgrounds land
COMPOSED_SLIDES_DIR	composed_slides	Where final slides land

Deploying to Vercel

The FastAPI wrapper (main.py) deploys to Vercel with zero configuration: Vercel auto-detects the FastAPI app (an app instance in main.py with fastapi in requirements.txt) and serves it as a single Python 3.12 function. vercel.json only raises the function timeout to 60s.

vercel --prod

Notes for serverless:

• The working directory is read-only on Vercel. Output directories (generated_images/, composed_slides/) automatically fall back to the writable /tmp area, so the app imports and runs without crashing.
• The antigravity CLI is not available in the function. Set GENERATION_PROVIDER=endpoint (an external image API) or AI_GENERATION_FALLBACK=true (placeholder images) in the project's Environment Variables, otherwise image-generation endpoints return 502.
• Artifacts written to /tmp are ephemeral - download them before the invocation ends if you need to keep them.
• Long image generations may exceed the function timeout; maxDuration is set to 60s in vercel.json.

Project structure

mcp_server.py            FastMCP server (stdio), 3 tools
main.py                  FastAPI wrapper (HTTP) - Vercel auto-detects this
services/
  copywriter.py          High-EEAT content strategy engine
  ai_generator.py        antigravity CLI / endpoint image engine (4K upscale)
  compositing.py         2160x2700 premium typography compositor
requirements.txt         Python dependencies
_mcp_smoke.py            In-process smoke test for all tools

Troubleshooting

• Server not listed / tools missing - restart the client; MCP config is read at startup.
• antigravity CLI not found - install the CLI on PATH, or set GENERATION_PROVIDER=endpoint, or AI_GENERATION_FALLBACK=true for placeholder renders.
• Slow first call - 4K image generation + Gaussian-blur compositing takes several seconds per slide; this is expected.
• Client on another machine - replace C:\mcp-cursoal with the cloned repo path and ensure Python/uv exist there.