Happy Horse MCP Server
Happy Horse MCP Server lets you generate and edit AI videos via Ace Data Cloud, with model discovery and asynchronous task polling.
Generate videos from text prompts.
Animate a first-frame image into video.
Generate videos from 1–9 reference images.
Edit a source video using text plus up to 5 optional reference images.
Query one task or batch up to 50 tasks for status and final video URLs.
List supported actions, compatible models, and defaults.
Supports 720P/1080P output, 3–15 second generations, async polling, and OAuth/API-token/stdio/hosted HTTP access.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Happy Horse MCP Server Generate a 720P, 9:16 video of a white horse crossing a snowy ridge at sunrise."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
HappyHorse by Ace Data Cloud
Model Context Protocol server for Happy Horse AI video generation and editing through the Ace Data Cloud API.
Use HappyHorse in Glama
Find the Ace-provided HappyHorse source listing or remote connector. Follow the Glama installation, API Token and first-use guide. Public discovery and a successful personal connection are separate checks; generation uses your Ace account balance.
Related MCP server: WanMCP
HappyHorse in goose
Use the independent HappyHorse by Ace Data Cloud native goose plugin: install and API Token guide · 简体中文. It reuses this MCP server and does not imply goose Marketplace or default-provider inclusion.
Find the HappyHorse integration in the official MCP Registry
Open HappyHorse in the official MCP Registry, expand the matching io.github.AceDataCloud entry, and use its Packages or Remotes installation information in a client that consumes MCP catalogs. This is the HappyHorse integration provided by Ace Data Cloud; it is not an endorsement by the model vendor. The Registry is a directory, not the app that runs a tool.
Search for mcp-happyhorse (or io.github.AceDataCloud/mcp-happyhorse) to locate the exact entry. The official Registry search currently matches server names; spaces in a display title do not create extra search aliases. If your client does not consume the catalog, use the client-specific connection instructions below and check Applications → General application for your token, service permissions, balance and current price. Keep the original task ID when checking asynchronous results.
Capabilities
Text-to-video generation
First-frame image-to-video animation
Reference-to-video generation with 1-9 subject or style images
Video editing with up to 5 reference images
720P and 1080P output
Single and batch task polling
Local stdio and hosted Streamable HTTP/SSE transports
Direct Bearer token and AceDataCloud OAuth authentication
Connect: hosted OAuth, API token, or local stdio
The hosted endpoint is https://happyhorse.mcp.acedata.cloud/mcp. Choose one route for the MCP client:
Route | When to use it | Credential setup |
Hosted OAuth | The client supports remote MCP OAuth | Add only the URL, then sign in to AceDataCloud and approve access. No token needs to be pasted into client configuration. |
Hosted API token | The client cannot finish OAuth, or you need an explicit integration credential | Send an AceDataCloud API token in the |
Local stdio | The client runs a local MCP process | Install |
The hosted service advertises OAuth metadata and Dynamic Client Registration (DCR). DCR registers the client application; it is not an API key. OAuth signs you in and the client sends the resulting Bearer token; it may reuse or create an API credential for the account. Browser sign-in still requires an AceDataCloud account. The hosted service can be metered: review current service documentation and displayed pricing before a real operation. Do not configure both an OAuth login and a fixed Authorization header for the same server.
Hosted OAuth examples
Claude and Claude Desktop chat: Add a remote custom connector in
Customize → Connectors → Add custom connector, enterhttps://happyhorse.mcp.acedata.cloud/mcp, select sign-in, and choose Register automatically if Claude asks how to register its OAuth client. Complete consent. Claude Desktop's localclaude_desktop_config.jsonis a separate setup. Claude connector guide.Claude Code:
claude mcp add --transport http --scope user happyhorse https://happyhorse.mcp.acedata.cloud/mcp, thenclaude mcp login happyhorse. Check/mcp. Claude Code MCP guide.Cursor: Add a remote server with only
https://happyhorse.mcp.acedata.cloud/mcp. For a project, merge the entry below into<project>/.cursor/mcp.json; for personal use, use~/.cursor/mcp.json. Cursor MCP guide.VS Code / Copilot: Run MCP: Add Server, select HTTP, enter
https://happyhorse.mcp.acedata.cloud/mcp, then finish the browser sign-in. New portable workspace configs use<project>/.mcp.json; the VS Code-specific format below uses<project>/.vscode/mcp.jsonor the user profile. Check MCP: List Servers. VS Code MCP setup.Codex:
codex mcp add happyhorse --url https://happyhorse.mcp.acedata.cloud/mcp, thencodex mcp login happyhorse. Its user settings are in~/.codex/config.toml. Official Codex MCP guide.
Cursor project config (OAuth):
{
"mcpServers": {
"happyhorse": {"url": "https://happyhorse.mcp.acedata.cloud/mcp"}
}
}VS Code-specific workspace config (OAuth):
{
"servers": {
"happyhorse": {"type": "http", "url": "https://happyhorse.mcp.acedata.cloud/mcp"}
}
}Hosted API token
Open Ace Data Cloud → Applications. Sign in, or register an account first.
Find General application. If it has not been created, choose Initialize General Application. A general key can use the services available to your account; a dedicated application's key is limited to its service. Check the required service/model access and current pricing.
Copy the API Key from the application card, or open Keys / Manage Keys → Create to create a separate key for this integration. Give it a recognizable name so you can revoke or rotate it independently.
When creating a key, review Expiration Time, Set Usage Limitation, and Allowed APIs. Enable only the restrictions you need, allow the operations you will call, then click Create and copy the token from the key list. Task lookup does not grant permission to submit a generation. Confirm the application's balance can cover your first request; any trial allowance is whatever your account currently displays.
Copy only the token value, without quotation marks, spaces, or
Bearer. An API Key / API Token field expects this raw value; only a manually configured HTTPAuthorizationheader needs theBearerprefix. Aplatform-...account-management token cannot replace a generation API key.


Save the key in the client's credential store or the environment variable shown below. Never put it in a prompt, shared workflow, screenshot, or Git commit. If authentication fails, check the full copied value, application/service access, expiration, usage limit, and balance before creating another key.
A fixed Bearer header bypasses OAuth. If the header is invalid, remove it before trying browser sign-in; keep the saved credential private.
For Claude Code, the shell expands the token when you add the server; treat the saved user MCP config as a secret:
export ACEDATACLOUD_API_TOKEN='YOUR_API_TOKEN'
claude mcp add --transport http --scope user happyhorse https://happyhorse.mcp.acedata.cloud/mcp \
--header "Authorization: Bearer $ACEDATACLOUD_API_TOKEN"For a Claude Code project config, put a variable reference in <project>/.mcp.json and set that variable in the environment that launches Claude Code:
{
"mcpServers": {
"happyhorse": {
"type": "http",
"url": "https://happyhorse.mcp.acedata.cloud/mcp",
"headers": {"Authorization": "Bearer ${ACEDATACLOUD_API_TOKEN}"}
}
}
}Cursor uses a different environment-variable syntax in ~/.cursor/mcp.json or an uncommitted project config:
{
"mcpServers": {
"happyhorse": {
"url": "https://happyhorse.mcp.acedata.cloud/mcp",
"headers": {"Authorization": "Bearer ${env:ACEDATACLOUD_API_TOKEN}"}
}
}
}In VS Code, run MCP: Open User Configuration and merge this server plus its masked input; ${input:...} is for VS Code's user/workspace format and is not portable to the Agent Host .mcp.json format:
{
"inputs": [
{"id": "acedata-happyhorse-token", "type": "promptString", "description": "AceDataCloud API token", "password": true}
],
"servers": {
"happyhorse": {
"type": "http",
"url": "https://happyhorse.mcp.acedata.cloud/mcp",
"headers": {"Authorization": "Bearer ${input:acedata-happyhorse-token}"}
}
}
}For Cline, use its MCP configuration UI or CLI file ~/.cline/data/settings/cline_mcp_settings.json; its remote transport value is streamableHttp. For JetBrains AI Assistant, add a remote URL from Settings → Tools → AI Assistant → Model Context Protocol (MCP). For Zed, use a context_servers entry with the URL only for OAuth or add a local Bearer header. These clients have different configuration schemas; follow their current UI rather than copying another client's JSON. Cline · JetBrains · Zed.
Local stdio
Install the package and give the local process an API token:
python -m pip install mcp-happyhorse
export ACEDATACLOUD_API_TOKEN='YOUR_API_TOKEN'
mcp-happyhorseFor Claude Desktop local MCP, merge this entry into the file opened by its developer settings (~/Library/Application Support/Claude/claude_desktop_config.json on macOS). uvx requires uv on PATH:
{
"mcpServers": {
"happyhorse": {
"command": "uvx",
"args": ["mcp-happyhorse"],
"env": {"ACEDATACLOUD_API_TOKEN": "YOUR_API_TOKEN"}
}
}
}Keep this user-level file private. Self-hosted HTTP uses mcp-happyhorse --transport http --port 8000; expose it only with suitable network and TLS controls. Local execution still calls the AceDataCloud API.
Check before using the service
https://happyhorse.mcp.acedata.cloud/healthreturning{"status":"ok"}checks endpoint reachability only.Confirm that the MCP client loads tools.
happyhorse_list_modelsis a reference tool; it does not verify downstream API access or balance.If you need a full API check, call
happyhorse_generate_videowith your own valid input after reviewing current service documentation and displayed pricing. If the result contains a task ID, callhappyhorse_get_taskon that same ID until terminal success or failure. Do not resubmit the operation just to check progress.
For 401, check which auth route the client used and whether the token or OAuth session is valid. A 403 may mean an account permission or content moderation failure; read the returned error. Insufficient balance and downstream service failures need their own diagnosis. A listed tool or submitted task does not prove a successful result.
Tools
Tool | Purpose |
| Generate a video from text |
| Animate one first-frame image |
| Generate from 1-9 reference images |
| Edit a source video with up to 5 references |
| Query one task |
| Query multiple tasks |
| List valid models for each action |
Generation tools submit asynchronously when no callback_url is supplied. Keep the returned
task_id, wait about 15 seconds, then call happyhorse_get_task until the response contains a
final video_url or terminal error.
Models
Action | Models | Default |
Text-to-video |
|
|
Image-to-video |
|
|
Reference-to-video |
|
|
Video edit |
|
|
Generation duration is 3-15 seconds. Supported resolutions are 720P and 1080P. Text and
reference generation support 16:9, 9:16, 1:1, 4:3, and 3:4. Image-to-video follows the
input image ratio. Video-edit duration follows the source video.
Example Requests
Ask your MCP client:
Generate a 720P, 9:16 video of a white horse crossing a snowy ridge at sunrise.
Animate https://example.com/horse.jpg with a slow camera push and wind moving the mane.
Edit https://example.com/source.mp4 to preserve the camera motion but apply the costume style from https://example.com/reference.jpg. Keep the original audio.
Environment
Variable | Default | Purpose |
| none | API token for local stdio mode |
|
| API origin |
|
| HTTP request timeout in seconds |
|
|
|
| none | Public URL that enables hosted OAuth |
|
| Logging level |
Development
pip install -e ".[all]"
pytest --cov=core --cov=tools
ruff check .
mypy core tools main.pyDocumentation
License
Cline setup / Cline 图文接入
HappyHorse by Ace Data Cloud — English guide · 完整中文图文步骤. The Cline marketplace submission is pending review as of 2026-10-18; the guide labels the manual fallback separately. / Cline 官方目录投稿仍待审核,手动接入不等于已上架。
API parameter behavior
Video editing inherits duration and aspect ratio from the source video. The compatibility duration and ratio arguments are ignored and are not forwarded. Image animation inherits its source image aspect ratio. Editing is billed by the combined input and output video duration, not a requested generation duration.
Available Tools
7 toolshappyhorse_edit_videoAInspect
Edit a source video using text and optional reference images.
Duration and aspect ratio follow the source video. Editing is billed by the combined input and output video duration, not the compatibility duration argument.
Inputs: prompt (required), video_url (required), seed, model, +7 more. Returns: result
happyhorse_generate_videoBInspect
Generate a video from a text prompt with Happy Horse.
Inputs: prompt (required), seed, model, ratio, +4 more. Returns: result
happyhorse_generate_video_from_imageCInspect
Animate a first-frame image with Happy Horse.
Inputs: image_url (required), seed, model, ratio, +5 more. Returns: result
happyhorse_generate_video_from_referencesBInspect
Generate a video guided by one or more reference images.
Inputs: prompt (required), image_urls (required), seed, model, +5 more. Returns: result
happyhorse_get_taskBInspect
Get the status and final video URL for one Happy Horse task.
Inputs: task_id (required). Returns: result
happyhorse_get_tasks_batchBInspect
Get multiple Happy Horse tasks in one request.
Inputs: task_ids (required). Returns: result
happyhorse_list_modelsBInspect
List Happy Horse actions, compatible models, and defaults.
Inputs: no named parameters. Returns: result
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 7 tools
Each tool targets a distinct Happy Horse operation: task lookup, batch task lookup, model listing, text-to-video generation, image animation, reference-guided generation, and video editing. The generation variants are clearly separated by input type, so an agent can select the right tool without ambiguity.
All tools use a consistent snake_case pattern with the shared happyhorse_ prefix and clear action-oriented names. The convention is predictable across task, model, and generation operations.
Seven tools is well-scoped for a video generation service, covering generation modes, editing, task inspection, batch inspection, and model discovery. Each tool has a clear purpose and earns its place.
The surface covers the core Happy Horse lifecycle: generating, editing, checking single or multiple tasks, and discovering models/defaults. Minor gaps such as task cancellation or deletion are not present but are unlikely to block common agent workflows.
Maintenance
Related MCP Connectors
Create and edit videos, images, voiceovers, music, and avatars with the VideoGen API.
Text, image and reference-to-video across many AI video models. Musevate account required.
One API for 100+ AI video, image, music and speech models.
Create and manage cinematic AI video renders through the Future Video Studio Agent API.
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