io.github.superlcr/hs MCP Server
io.github.superlcr/hs
Turn scripts into finished videos with AI-powered storyboarding, narration, and footage composition.
What is the io.github.superlcr/hs MCP server?
The Huasheng CLI (hs) MCP server brings Huasheng's video creation pipeline to the command line and AI clients. It automates the entire process from a script or sentence to a finished, publishable video by handling storyboarding, narration generation, footage selection, and composition. Available as a self-contained binary with MCP support for Claude Desktop, ChatGPT, Codex CLI, and Claude Code.
hs is a command-line tool and MCP server that transforms scripts or sentences into complete videos. It handles storyboarding, AI narration, stock footage selection, and video composition automatically. You can use it directly via CLI commands or integrate it into AI clients like Claude Desktop for conversational video creation. Every step supports JSON output for scripting and automation.
How to install io.github.superlcr/hs
Copy-paste configuration for popular MCP clients.
Tools & capabilities
Tools this server exposes to the agent.
hs make— Create a video from a script, sentence, or audio file with automatic storyboarding, narration, and footage compositionhs auth login— Authenticate with Huasheng and manage credentials shared across all clientshs account— Check credit balance and account informationhs upgrade— Update to the latest version of the CLIMCP server tools— Interactive tools for listing projects, inspecting storyboards, adjusting narration, replacing footage, changing narrator voice, and publishing videos through AI clients
Use cases
- Create short-form videos from text prompts or scripts for social media
- Generate videos with custom narration by providing your own audio files
- Batch-create multiple videos programmatically using JSON output and exit codes
- Interactively refine videos through AI clients by adjusting storyboards, footage, and narration
- Produce MG-style (motion graphics) videos with specific visual styles
io.github.superlcr/hs MCP server FAQ
It's a command-line tool and MCP server that automates video creation from scripts or sentences, handling storyboarding, narration, footage selection, and composition. You can use it directly or integrate it into AI clients like Claude Desktop.
No. Creating videos spends credits from your Huasheng account. Confirming storyboards, changing narration after production, and publishing videos all incur costs. You can check prices before approving.
Download huasheng.mcpb from the latest release, double-click it, select Install in Claude Desktop, and confirm the path to hs (default is ~/.local/bin/hs).
Cursor doesn't natively support MCP servers. Use Claude Desktop, ChatGPT Desktop, Codex CLI, or Claude Code instead. For CLI use, install via curl, PowerShell, or npm.
You need a Huasheng account. Sign in once with `hs auth login` and credentials are shared across all clients. No separate sign-in is needed for each AI client.
You can provide narration as mp3, wav, flac, mp4, or m4a files from your computer or a public URL. Transcripts are optional; hs will transcribe the audio if you don't provide one.
README (reference)
Source of truth, from the repository.
hs · Huasheng CLI
From one sentence to a finished, publishable video
简体中文 · English
</div>hs brings Huasheng's video creation pipeline to the command line.
Give it a sentence or a script, and it handles storyboarding, narration, footage and
composition — producing a video you can export or publish directly. You can step in and
adjust at any point along the way.
A single self-contained binary. No Node, no Python, no runtime to install.
Every command supports --json, designed for scripts and AI clients.
Step 1: install and sign in
Do this once, regardless of which client you use later.
Install
Pick whichever suits you — all three give you the same binary.
macOS / Linux
curl -fsSL https://raw.githubusercontent.com/superlcr/huasheng-cli/main/install.sh | sh
Windows, in PowerShell:
irm https://raw.githubusercontent.com/superlcr/huasheng-cli/main/install.ps1 | iex
Either installer downloads the package for your platform, verifies its SHA256, and extracts
it to ~/.local/bin (%LOCALAPPDATA%\Programs\hs on Windows).
With npm, if you already live in the Node ecosystem or just want to try it first:
npx @superlcr/hs --help
npm install -g @superlcr/hs # the command is still `hs`
The npm package is a small launcher; the binary for your platform arrives as an optional dependency, so nothing is downloaded or compiled at install time.
<details> <summary>Manual download instead of the installer</summary> <br>Grab the package for your platform from
Releases, extract it, and place
the executable anywhere on your PATH:
| Platform | File |
|---|---|
| macOS · Apple Silicon | hs-darwin-arm64.tar.gz |
| macOS · Intel | hs-darwin-x64.tar.gz |
| Linux · x64 | hs-linux-x64.tar.gz |
| Windows · x64 | hs-windows-x64.zip |
Every release ships a SHA256SUMS file. Verifying it is recommended:
shasum -a 256 -c SHA256SUMS
</details>Both macOS packages are signed and notarized by Apple (Developer ID Application). The Windows package is unsigned; SmartScreen may prompt on first run — choose "More info → Run anyway".
Sign in
Open a new terminal, sign in, and confirm that hs can read your credit balance:
hs auth login
hs account
The CLI and every AI client share ~/.hs/credentials.json; you do not sign in separately.
No browser on that machine (SSH, a cloud server, a container)? Run hs auth login --no-browser, open
the address it prints on any device, approve, and paste back the address the browser lands on (it
starts with http://127.0.0.1 and will not load — that is expected).
Step 2: choose how you use it
Both paths use the same hs binary and the same sign-in — pick either, or both.
Option 1: use hs CLI directly
Use this path for exact commands, scripts, or batch jobs. hs make can start from one sentence or
a complete script, run the creation workflow, wait for the finished video, and download it.
Create a video from one sentence:
hs make --script "Three little-known facts about West Lake" --out ./out.mp4
Request an MG-style video:
hs make --script "Explain Song dynasty tea whisking in 30 seconds" --mode mg --out ./tea.mp4
Read a long script from a file:
hs make --script @script.txt --out ./video.mp4
Or start from your own narration recording — a file on your computer or a public URL:
hs make --audio ./narration.m4a --out ./video.mp4
hs make --audio ./narration.m4a --transcript @words.txt --out ./video.mp4 # if you have the words
Huasheng keeps your voice and cuts footage to it. The transcript is optional; without it, Huasheng
transcribes the recording. Supported formats are mp3, wav, flac, mp4 and m4a; hs uploads the
file itself and you never need an internal storage address.
hs make approves the storyboard for you — that spends credits, and it prints how many. To read
the storyboard and its price first, use the step-by-step commands instead. See the
hs CLI guide for parameters, step-by-step editing, resuming, and exporting. See
Scripting and automation for JSON, exit codes, and batches.
Option 2: use hs through MCP in an AI client
hs includes an MCP server. Any AI client that supports local STDIO MCP can launch it with:
{
"mcpServers": {
"huasheng": {
"command": "hs",
"args": ["mcp", "serve"]
}
}
}
This configuration simply tells the client to run hs mcp serve when Huasheng is needed. There is
no separate hs MCP package to install, and you should not keep the command running yourself. If the
client cannot find hs, replace command with the full path from which hs (where hs on Windows).
The following are setup examples for four common clients. For any other MCP client, enter the same
command and args in its MCP server settings.
ChatGPT Desktop App
- Open Settings → MCP servers → Add server
- Enter
huashengand choose STDIO - Set Command to the full path to
hs; addmcpandserveas the two arguments - Save and restart, then type
/mcpand check thathuashengis connected
ChatGPT Desktop renders interactive timeline, preview, footage, and export cards. It shares
~/.codex/config.toml with Codex CLI, so this setup also enables hs there.
Claude Desktop App
- Download huasheng.mcpb
- Double-click it, then select Install in Claude Desktop
- Confirm the path to
hs; the default is~/.local/bin/hs
If you changed the install location, paste the full path from which hs (where hs on Windows).
Continue if the first install warns that the extension is unsigned. Claude Desktop also renders
interactive cards.
Codex CLI
codex mcp add huasheng -- hs mcp serve
codex mcp list
Do not add it again if you already configured huasheng in ChatGPT Desktop; both read
~/.codex/config.toml. See the OpenAI MCP documentation.
Claude Code CLI
claude mcp add --scope user huasheng -- hs mcp serve
claude mcp list
Both commands run in the same terminal where you just signed in, so plain hs resolves; if your
shell cannot find it, substitute the full path from which hs (where hs on Windows). Codex CLI
and Claude Code present complete text results instead of desktop interactive cards.
Use it through conversation
After setup, say in your AI client:
Make me a 30-second video about why the sky is blue
You can inspect and refine existing projects too:
List my recent Huasheng projects
Make the narration in clip 2 shorter
Replace clip 3 with more futuristic footage
Use my own file ./b-roll.mp4 for clip 2
Change the narrator to a warmer voice, and tell me the price first
Confirming a storyboard spends credits, and publishing makes the video public. hs marks those
and the other one-way tools (deleting a project, footage or a preference, joining the priority lane)
as destructive, so a client that confirms destructive tools asks you first; hs itself does not
prompt. Changing the voice after production and adding footage to the library spend credits too;
the MCP tools quote the price when asked.
More documentation
- hs CLI guide: step-by-step creation, states, and command groups
- Scripting and automation: JSON, exit codes, batch control, and IDs
- Sign-in, privacy, and requirements: credentials, network boundaries, platforms
Safety boundaries
- The CLI and every AI client share one local credential;
hsnever receives your Bilibili password. - Approving a storyboard spends credits, and publishing goes public. So do changing the voice after
production and adding footage to the library, and those credits are not refunded.
hsdoes what the command says and reports the cost; whether you are asked first is up to you, your script, or your AI client. - Scripts, narration recordings, and footage are uploaded to Huasheng for video creation; there is no separate telemetry channel or background updater. The only other connection is a once-a-day check of the latest release number on GitHub, which sends nothing but the hs version and can be turned off.
Upgrading
hs upgrade
For an installer build, this downloads the latest release, checks its SHA256, and swaps it in; for
an npm install it runs npm i -g @superlcr/hs@latest. hs never downloads or replaces itself on
its own. Once a day it asks for the latest release number and mentions it if yours is older; set
HS_NO_UPDATE_CHECK=1 to turn that off.
AI clients that run hs mcp serve keep the old process until they restart. After upgrading, start
a new session in Claude Code or Codex, or quit and reopen Claude Desktop; until then the client is
still on the old version. Upgrading while a client is running is safe on every platform.
Feedback
Found a problem? Please open an
issue and include the output of
hs --version — it carries the commit and build time, which is the key to diagnosing anything.
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