Seedream MCP Server
Enables generating AI images using BytePlus Seedream models (4.5 and 5.0 Lite) via natural language, supporting text-to-image, reference-based editing, and sequential multi-panel sets.
README
Seedream
Generate AI images with BytePlus Seedream 4.5 and Seedream 5.0 Lite — from your terminal, your AI client, or Claude Code.
Choose your path
| I want to… | Use |
|---|---|
| Talk to Claude in natural language (Claude Desktop, Cursor, Windsurf) | MCP Server |
| Generate images from the terminal | CLI |
| Use slash commands inside Claude Code | Claude Code Skill |
All three share the same credentials and the same underlying pipeline. The Seedream image API is synchronous — a single call returns the finished image URL(s). There is no task to poll.
Models
| Model | ID | Sizes | Notes |
|---|---|---|---|
| Seedream 4.5 | seedream-4-5-251128 |
2K, 4K | Default. No 1K. |
| Seedream 5.0 Lite | seedream-5-0-260128 |
1K, 2K, 4K, adaptive | Newer, cheaper, stronger reference consistency & layout reasoning. |
Aliases 4.5 and 5.0-lite are accepted anywhere a model is specified.
Prerequisites
Python 3.10+, a local clone of this repo, and keys from the BytePlus console:
| Key | Where | Used for |
|---|---|---|
ARK_API_KEY |
ModelArk → API Keys | Image generation (required) |
ARK_AK |
IAM → Access Keys | TOS upload of local reference images (optional) |
ARK_SK |
IAM → Access Keys | TOS upload of local reference images (optional) |
SEEDREAM_TOS_BUCKET |
TOS console → Buckets | Default bucket for uploads (optional) |
SEEDREAM_TOS_REGION |
— | TOS region, default ap-southeast-1 (optional) |
Plain text-to-image (and references you pass as public URLs) need only ARK_API_KEY.
🖥 MCP Server
Lets Claude Desktop, Cursor, and Windsurf generate images from natural language.
Setup
git clone https://github.com/mittulmadaan/byteplus-seedream-mcp
cd byteplus-seedream-mcp
pip install packages/seedream-sdk packages/seedream-mcp -e packages/seedream-cli
seedream auth login # stores credentials in ~/.seedream/credentials
seedream skill install # patches Claude Desktop + Claude Code configs automatically
# Restart your AI client
Then just ask Claude: "Generate a 4K poster of iced cold brew on a sunlit table with the headline Summer Cold Brew"
Manual config
Add to ~/Library/Application Support/Claude/claude_desktop_config.json (Mac) or the
equivalent on your OS:
{
"mcpServers": {
"seedream": {
"command": "/path/to/.venv/bin/python",
"args": ["-m", "seedream_mcp"]
}
}
}
Set credentials via environment variables before launching your client.
Hosted / Docker
docker build -f packages/seedream-mcp/docker/Dockerfile -t seedream-mcp .
docker run -p 8000:8000 \
-e MCP_TRANSPORT=sse \
-e ARK_API_KEY=<key> \
-e ARK_AK=<ak> \
-e ARK_SK=<sk> \
-e MCP_AUTH_TOKEN=<strong-random-token> \
seedream-mcp
Your BytePlus credentials stay server-side. Clients connect using MCP_AUTH_TOKEN only.
Point your MCP client at http://your-host:8000/mcp (streamable HTTP transport).
How to use
Use seedream_generate for everything — it returns image URLs directly:
# Text-to-image
seedream_generate(prompt="A silver espresso machine, morning light, steam rising", size="2K")
→ { model: "seedream-4-5-251128", count: 1, images: [{ url: "https://..." }] }
# Seedream 5.0 Lite, 4K
seedream_generate(prompt="Editorial close-up portrait, sculptural hat, studio lighting",
model="5.0-lite", size="4K")
# Editing / reference — say what to change and what to keep
seedream_generate(
prompt="Use Image 1. Replace the background with a snowy forest at dusk. Keep the
subject's pose, outfit, and the product label unchanged.",
image_urls=["https://example.com/photo.png"]
)
# Consistent multi-panel set
seedream_generate(prompt="A 4-panel comic of a cat's morning, flat pastel style",
sequential=true, num_images=4)
For a local reference image, upload it first:
seedream_list_tos_buckets() # ask which bucket
seedream_upload_image_to_tos(file_path="/path/ref.png", bucket="<name>")
→ { url: "https://<bucket>.tos-....bytepluses.com/ref.png" }
# then pass that url in image_urls
Available tools
| Tool | Description |
|---|---|
seedream_generate |
Start here. Text-to-image, editing, reference, sequential sets. Returns URL(s). |
seedream_upload_image_to_tos |
Upload a local image; returns a public URL. |
seedream_list_tos_buckets |
List TOS buckets so Claude can ask which to use. |
seedream_list_models |
List models, sizes, and features. |
seedream_ping |
Verify credentials and connectivity. |
💻 CLI
Full-featured terminal interface.
Setup
git clone https://github.com/mittulmadaan/byteplus-seedream-mcp
cd byteplus-seedream-mcp
pip install packages/seedream-sdk packages/seedream-mcp -e packages/seedream-cli
seedream auth login
Generate an image
# Text-to-image
seedream generate -p "A serene mountain landscape at sunset with golden clouds" \
--size 2K --output out/
# Seedream 5.0 Lite, 4K
seedream generate -p "Editorial portrait, dramatic studio lighting" \
--model 5.0-lite --size 4K --output out/
# Editing / reference — local path auto-uploads to TOS
seedream generate -p "Replace the background with a snowy forest. Keep the pose." \
--ref ./photo.png --bucket my-bucket --output out/
# Consistent multi-panel set
seedream generate -p "A 4-panel comic of a cat's morning, flat pastel style" \
--sequential -n 4 --output out/
All flags
seedream generate \
--prompt "..." # description / edit instruction (required)
--model 5.0-lite # 4.5 (default) | 5.0-lite | full model ID
--size 2K # 1K* | 2K | 4K | adaptive* | WxH (*5.0 Lite only)
--ref <url|path> # reference image; repeatable; local paths auto-upload
--bucket <name> # TOS bucket for local --ref uploads
--seed 42 # reproducible results
--guidance-scale 7.5 # prompt adherence 1.0–20.0
--sequential # generate a consistent multi-panel set
--num-images 4 # images in a sequential set (refs + n ≤ 15)
--optimize-prompt # let the API optimize the prompt
--watermark # add a watermark
--output out/ # download the image(s) into this directory
Other commands
# Auth
seedream auth login # store credentials interactively
seedream auth check # verify keys are configured
seedream auth logout
# TOS object storage — upload a LOCAL file, get a public URL
seedream tos buckets # list buckets
seedream tos upload ./photo.png --bucket <name> # upload, prints public URL
# Info
seedream models # list models, sizes, features
seedream ping # verify credentials and connectivity
# Skill management
seedream skill install # install into Claude Desktop + Claude Code
seedream skill check # show install status
seedream skill uninstall
🧠 Claude Code Skill
Slash commands inside Claude Code, backed by the MCP server.
Setup
git clone https://github.com/mittulmadaan/byteplus-seedream-mcp
cd byteplus-seedream-mcp
pip install packages/seedream-sdk packages/seedream-mcp -e packages/seedream-cli
seedream auth login
seedream skill install # copies skill file + patches Claude Code settings
# Restart Claude Code
The skill lives in skills/seedream.md with detailed prompting,
editing, and model-selection guidance in skills/references/.
SDK
For building custom integrations:
from seedream import SeedreamClient
client = SeedreamClient() # reads from env vars or ~/.seedream/credentials
# Text-to-image — synchronous, returns URLs directly
result = client.generate(
prompt="A sunrise over mountains, warm light",
model="4.5",
size="2K",
)
print(result.urls)
# Editing with a reference image
result = client.generate(
prompt="Replace the background with a beach. Keep the subject unchanged.",
image_urls=["https://example.com/photo.png"],
model="5.0-lite",
)
# Generate and download to a directory
result = client.generate_and_download(
"A 4-panel comic of a cat's day", "out/",
sequential=True, num_images=4,
)
for img in result.images:
print(img.local_path)
Install: pip install packages/seedream-sdk (zero runtime dependencies — stdlib urllib only).
Repo Structure
packages/
seedream-sdk/ # core: HTTP client, TOS HMAC signing, config, types (zero deps)
seedream-mcp/ # MCP server — thin wrappers over SDK
docker/ # Dockerfile + docker-compose
seedream-cli/ # CLI — typer + rich
seedream_cli/commands/
auth.py generate.py tos.py models.py skill.py
skills/
seedream.md # Claude Code skill definition
references/ # prompting, editing, sequential sets, models, assets
.github/workflows/
ci.yml # lint + test (Python 3.10 / 3.11 / 3.12)
release-sdk.yml # → PyPI on tag sdk/v*
release-mcp.yml # → PyPI + Docker Hub on tag mcp/v*
release-cli.yml # → PyPI on tag cli/v*
Credentials
Resolution order (first non-empty value wins):
- Constructor args:
SeedreamClient(api_key="...", ak="...", sk="...") - Environment variables:
ARK_API_KEY(orBYTEPLUS_API_KEY),ARK_AK,ARK_SK ~/.seedream/credentials— written byseedream auth login.envfile in CWD (requirespython-dotenv)
Never embed credentials in client config files or Docker images. Use
MCP_AUTH_TOKENto protect hosted SSE endpoints.
Release
git tag sdk/v1.0.1 && git push --tags # publishes seedream-sdk to PyPI
git tag mcp/v1.0.1 && git push --tags # publishes seedream-mcp to PyPI + Docker Hub
git tag cli/v1.0.1 && git push --tags # publishes seedream-cli to PyPI
Support
Note: Seedream model IDs may change as BytePlus ships new versions. If a call returns an unknown-model error, re-check the docs above and update
packages/seedream-sdk/seedream/config.py.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.