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Glama

HappyHorse by Ace Data Cloud

简体中文安装与首用指南

PyPI Python License

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 Authorization: Bearer … header. Keep it in a local secret store or environment variable.

Local stdio

The client runs a local MCP process

Install mcp-happyhorse and pass ACEDATACLOUD_API_TOKEN to that process. It still calls the AceDataCloud API.

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, enter https://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 local claude_desktop_config.json is a separate setup. Claude connector guide.

  • Claude Code: claude mcp add --transport http --scope user happyhorse https://happyhorse.mcp.acedata.cloud/mcp, then claude 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.json or the user profile. Check MCP: List Servers. VS Code MCP setup.

  • Codex: codex mcp add happyhorse --url https://happyhorse.mcp.acedata.cloud/mcp, then codex 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

  1. Open Ace Data Cloud → Applications. Sign in, or register an account first.

  2. 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.

  3. 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.

  4. 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.

  5. 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 HTTP Authorization header needs the Bearer prefix. A platform-... account-management token cannot replace a generation API key.

Copy an application API key or open Manage Keys

Create a separately named key with optional restrictions

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-happyhorse

For 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

  1. https://happyhorse.mcp.acedata.cloud/health returning {"status":"ok"} checks endpoint reachability only.

  2. Confirm that the MCP client loads tools. happyhorse_list_models is a reference tool; it does not verify downstream API access or balance.

  3. If you need a full API check, call happyhorse_generate_video with your own valid input after reviewing current service documentation and displayed pricing. If the result contains a task ID, call happyhorse_get_task on 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

happyhorse_generate_video

Generate a video from text

happyhorse_generate_video_from_image

Animate one first-frame image

happyhorse_generate_video_from_references

Generate from 1-9 reference images

happyhorse_edit_video

Edit a source video with up to 5 references

happyhorse_get_task

Query one task

happyhorse_get_tasks_batch

Query multiple tasks

happyhorse_list_models

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

happyhorse-1.0-t2v, happyhorse-1.1-t2v

happyhorse-1.1-t2v

Image-to-video

happyhorse-1.0-i2v, happyhorse-1.1-i2v

happyhorse-1.1-i2v

Reference-to-video

happyhorse-1.0-r2v, happyhorse-1.1-r2v

happyhorse-1.1-r2v

Video edit

happyhorse-1.0-video-edit

happyhorse-1.0-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

ACEDATACLOUD_API_TOKEN

none

API token for local stdio mode

ACEDATACLOUD_API_BASE_URL

https://api.acedata.cloud

API origin

HAPPYHORSE_REQUEST_TIMEOUT

60

HTTP request timeout in seconds

MCP_TRANSPORT

stdio

stdio or http

MCP_SERVER_URL

none

Public URL that enables hosted OAuth

LOG_LEVEL

INFO

Logging level

Development

pip install -e ".[all]"
pytest --cov=core --cov=tools
ruff check .
mypy core tools main.py

Documentation

Documentation

License

MIT

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 tools
happyhorse_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

    A3.6/5.0

    Scored across 7 tools

    Disambiguation5/5

    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.

    Naming Consistency5/5

    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.

    Tool Count5/5

    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.

    Completeness4/5

    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

    ActivityActive
    ResponsivenessNo recent community issues

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