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MCP server · Content

visualgen-mcp

An MCP server that enables Claude Code to generate images and short videos using Google's Gemini API (Imagen 4 for stills, Veo 3.1 for video). It integrates directly into development workflows, allowing AI assistants to create visual assets and reference them in code without context switching.

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About visualgen-mcp

visualgen-mcp is an MCP server in the Content category: an MCP server that enables Claude Code to generate images and short videos using Google's Gemini API (Imagen 4 for stills, Veo 3.1 for video). It integrates directly into development workflows, allowing AI assistants to create visual assets and reference them in code without context switching. It has been installed 0 times through Conduid.

Install

Clone
git clone https://github.com/1RaghavM/visualgen-mcp

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README

visualgen-mcp

An MCP server that lets Claude Code generate images and short videos via Google's Gemini API (Imagen 4 for stills, Veo 3.1 for video).

Why this exists

I was building websites with Claude Code and kept hitting the same gap. Claude would design a great layout, then say "add your hero image here." I'd go generate one somewhere else, come back, wire it up, iterate, repeat. The context switching was the whole cost. So I built this MCP server to close the loop. Claude writes the prompt, the server hits Veo or Imagen, the file lands in the project directory, and Claude references it in the code it's already writing. One session, no handoffs.

I'm open-sourcing it because other people are solving the same problem, and nobody should have to build this twice.

Demo

Claude Code generating visuals with visualgen-mcp and wiring them into the page.

https://github.com/user-attachments/assets/34d25755-cd4c-47b0-a0cb-056b9ed8b783

What it does

Six tools, exposed over stdio:

  • submit_video(prompt, model, aspect_ratio, resolution, negative_prompt?, image_path?) — submit a Veo 3.1 job. Returns a job_id instantly.
  • check_video(job_id) — poll a job. When the video is ready, the server downloads it and returns the path.
  • list_videos() — list MP4s in the output directory, newest first.
  • generate_image(prompt, model, aspect_ratio, negative_prompt?) — synchronous image generation (typically under 10s). Returns the PNG path.
  • list_images() — list PNGs in the output directory.
  • get_pricing() — return current Gemini rates so Claude can warn you before expensive runs.

What it costs

Gemini API rates as of 2026-04-22. Verify at the official pricing page before production use.

Model Cost
Veo 3.1 Standard (720p / 1080p) $0.40 / second
Veo 3.1 Standard (4k) $0.60 / second
Veo 3.1 Fast (720p) $0.10 / second
Veo 3.1 Fast (1080p) $0.12 / second
Veo 3.1 Fast (4k) $0.30 / second
Veo 3.1 Lite (720p) $0.05 / second
Veo 3.1 Lite (1080p) $0.08 / second
Imagen 4 Fast $0.02 / image
Imagen 4 Standard $0.04 / image
Imagen 4 Ultra $0.06 / image
Gemini 2.5 Flash Image (Nano Banana) $0.039 / image

Install

For end users:

uvx visualgen-mcp

For contributors (from source):

git clone https://github.com/1RaghavM/visualgen-mcp.git
cd visualgen-mcp
uv sync
uv run python -m visualgen_mcp

Configure

Run the interactive setup once. It saves your profile to ~/.config/visualgen-mcp/config.toml (chmod 600) and optionally wires up .mcp.json in the current project directory.

uvx visualgen-mcp init

You'll be prompted for:

  • Gemini API key — get one at aistudio.google.com/apikey. Veo requires a paid plan; Imagen and Nano Banana work on the free tier but with stricter rate limits.
  • Output directory — where generated PNGs and MP4s land. Defaults to ~/visualgen-output. The server creates it if it doesn't exist, and tool responses return absolute paths so Claude can reference the files in the code it writes.
  • Default video tier, image model, and aspect ratios — used when Claude calls a tool without specifying these.

Re-run visualgen-mcp init any time to update the profile. Per-project overrides still work: a GEMINI_API_KEY or OUTPUT_DIR set in a local .env file or in .mcp.json's env block takes precedence over the profile.

Use with Claude Code

Either run visualgen-mcp init inside your project (it offers to write this for you), or add this to .mcp.json at the root of your project:

{
  "mcpServers": {
    "visualgen": {
      "command": "uvx",
      "args": ["visualgen-mcp"]
    }
  }
}

The server reads your API key and defaults from ~/.config/visualgen-mcp/config.toml. If you want to override them for a specific project, set GEMINI_API_KEY or OUTPUT_DIR in .mcp.json's env block — env vars take precedence over the profile.

Run /mcp inside Claude Code to confirm the server is connected. You should see visualgen listed with six tools.

The /visualgen skill

The server ships with a Claude Code skill that teaches Claude when to use each tool, how to structure prompts for common web-design asks, and when to confirm costs with you. If you ran visualgen-mcp init, it already offered to install it. Otherwise:

cd your-project
cp -r /path/to/visualgen-mcp/.claude/skills/visualgen ./.claude/skills/visualgen

Once installed, /visualgen hero video, modern SaaS landing, calm mood kicks off the whole pipeline in one shot — Claude picks the template, builds the prompt, submits the job, and wires the returned file into your code. You can also just ask for visuals in natural language — the skill auto-loads whenever Claude is about to call a visualgen-mcp tool.

The skill is opinionated about cost: Veo standard and any image-to-video call always ask you to confirm before spending. Everything else — all image generation plus Veo lite/fast text-to-video — proceeds silently.

Use with other MCP clients

Claude Desktop uses the same pattern. Add an entry under mcpServers in ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows). Any stdio-transport MCP client that supports tool calls works. There is no HTTP or auth layer — the server is intended for local use.

Example session

A real run from a landing-page project. Elided lines marked ....

> Build me a hero video loop for the top of the page. Something that suggests "city at night, calm, slow."

I'll generate that with the visualgen server.

  submit_video(
    prompt="A slow drone shot over a city skyline at night, warm window lights,
            occasional headlights tracing streets below, calm mood, cinematic",
    model="fast",
    aspect_ratio="16:9",
    resolution="1080p"
  )
  → { "job_id": "f3c1...", "status": "submitted", "model": "veo-3.1-fast-generate-preview",
      "estimated_seconds": 60 }

I'll check back in a minute.

  check_video("f3c1...")
  → { "status": "pending", "elapsed_seconds": 25 }

  ... wait ...

  check_video("f3c1...")
  → { "status": "complete",
      "path": "/Users/.../landing/generated/veo-fast-20260422T191203Z.mp4",
      "duration_seconds": 8.0 }

Writing it into the hero section:

  Edit: src/components/Hero.tsx
    <video
      autoPlay
      muted
      loop
      playsInline
      className="absolute inset-0 h-full w-full object-cover"
      src="/generated/veo-fast-20260422T191203Z.mp4"
    />

One prompt, one generation, file in the project, code updated.

Tool reference

submit_video

Param Type Default Notes
prompt str (required) Video description.
model str "fast" "lite", "fast", or "standard".
aspect_ratio str "16:9" "16:9" or "9:16".
resolution str "720p" "720p", "1080p", "4k". "4k" rejected for "lite".
negative_prompt str | None None Text describing what to avoid.
image_path str | None None Absolute path to a PNG/JPEG/WebP starting frame.

Returns {"job_id": str, "status": "submitted", "model": str, "estimated_seconds": int}.

check_video

Param Type Default
job_id str (required)

Returns one of:

  • {"status": "pending", "elapsed_seconds": int}
  • {"status": "complete", "path": str, "duration_seconds": float}
  • {"status": "failed", "error": str}

list_videos

No parameters. Returns list[{"path": str, "size_bytes": int, "created_at": str}], newest first.

generate_image

Param Type Default Notes
prompt str (required) Image description.
model str "nano-banana" "nano-banana" or "imagen".
aspect_ratio str "16:9" "1:1", "16:9", "9:16", "4:3", "3:4".
negative_prompt str | None None Ignored by Nano Banana.

Returns {"path": str, "model_used": str}.

list_images

No parameters. Returns list[{"path": str, "size_bytes": int, "created_at": str}], newest first.

get_pricing

No parameters. Returns a dict of current rates per model, a last_updated date, and a link to the source. Hardcoded — the server does not call an API for this.

Limits and gotchas

  • Veo clips are capped at 8 seconds. This server always requests 8 seconds.
  • There is no free tier for Veo. Every successful generation is billed.
  • Generated videos are deleted from Google's servers 48 hours after generation. Download them (via check_video) within that window.
  • The job store is in-memory. If you restart the server, pending jobs are lost. See the TODO in src/visualgen_mcp/jobs.py for the SQLite migration path.
  • Some prompts are rejected by Google's content filters. You get {"status": "failed", ...} back, not a crash.
  • Nano Banana (gemini-2.5-flash-image) is faster and cheaper than Imagen 4 but does not accept a negative_prompt. Use Imagen 4 when you need one.
  • list_videos and list_images only see files in OUTPUT_DIR. They do not recurse or cross directories.

Contributing

See CONTRIBUTING.md.

License

MIT. See LICENSE.

README mirrored from the source repository 4 months ago. The original is authoritative.

Questions

About visualgen-mcp

How do I install visualgen-mcp?

Run git clone https://github.com/1RaghavM/visualgen-mcp, then add the server to your MCP client's configuration. Conduid has recorded 0 installs, so the command is known to work with current clients.

Is visualgen-mcp safe to use with an AI agent?

Its trust score is 34 out of 100 (low). Conduid hasn't run static security checks on this repository yet, so review the source yourself before granting it credentials. It has no ConduID identity yet, so agent calls to it are not receipted.

Is visualgen-mcp still maintained?

Conduid hasn't recorded a commit date for this repository yet. Check the repository directly for recent activity.