About happyhorse-mcp
happyhorse-mcp is a RunAPI MCP server for the HappyHorse model line. It lets MCP clients create video editing, text-to-video, and image-to-video tasks, track asynchronous status, and check pricing.
Key features
- 01Create video editing tasks
- 02Create text-to-video tasks
- 03Create image-to-video tasks
- 04Check task status and results
- 05Look up model and endpoint pricing
Use cases
- 01Submit a video editing task
- 02Generate video from text
- 03Turn an image into video
- 04Check pricing before submission
MCP support
The MCP server exposes HappyHorse task creation, status lookup, and pricing tools to MCP clients.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
npx -y @runapi.ai/happyhorse-mcpRUNAPI_API_KEYSecretOptional RunAPI API key for headless hosts. Interactive MCP hosts can use the login tool, which writes shared RunAPI config.Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.
Tools
6Saved tool list · updated 1w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| force | boolean | No | Re-run browser login when the current credential comes from the local config file. |
Details
From the project READMERunAPI HappyHorse MCP Server
HappyHorse API access for AI agents: run video generation operations, poll asynchronous results, and check pricing through one focused MCP server.
Works with Claude Code, Codex, Cursor, Windsurf, VS Code, Roo Code, and any MCP-compatible host.
Install | Tools | Models | Agent Prompts | Configuration | Links
Why This Package?
@runapi.ai/happyhorse-mcp is a focused Model Context Protocol server for the HappyHorse model line on RunAPI.
It gives MCP-compatible assistants direct access to 3 endpoints and 7 model variants without loading the full RunAPI catalog.
Use this per-model server when an agent should stay scoped to HappyHorse. Use @runapi.ai/mcp when one assistant should discover every RunAPI model line.
Install
Add it to Claude Code:
claude mcp add happyhorse -s user -- npx -y @runapi.ai/happyhorse-mcp
Use project scope when the server should be shared with a repository:
claude mcp add happyhorse -s project -- npx -y @runapi.ai/happyhorse-mcp
Codex, Cursor, Windsurf, VS Code, Roo Code, and other MCP hosts can use the same stdio command:
{
"mcpServers": {
"happyhorse": {
"command": "npx",
"args": ["-y", "@runapi.ai/happyhorse-mcp"]
}
}
}
check_pricing works before sign-in. For task creation and status polling, ask your assistant to call the login tool. It opens a browser login and saves credentials to ~/.config/runapi/config.json, the same file used by runapi login.
Headless and CI hosts can still set RUNAPI_API_KEY before starting the MCP host.
Ready-made examples are in examples/ for Claude, Cursor, Windsurf, VS Code, and Roo Code.
Tools
| Tool | Auth | Purpose |
|---|---|---|
edit_video | Yes | Create a HappyHorse edit video task and optionally wait for a terminal status. Returns the task id, status, and output URLs. |
image_to_video | Yes | Create a HappyHorse image to video task and optionally wait for a terminal status. Returns the task id, status, and output URLs. |
text_to_video | Yes | Create a HappyHorse text to video task and optionally wait for a terminal status. Returns the task id, status, and output URLs. |
get_task | Yes | Fetch the current status and latest payload for an existing task. |
check_pricing | No | Look up current pricing for a HappyHorse model and endpoint. |
Models
HappyHorse covers 7 model variants across 3 endpoints. Each tool accepts the models listed for it:
| Tool | Models |
|---|---|
edit_video | happyhorse-edit-video |
image_to_video | happyhorse-1.0-i2v, happyhorse-image-to-video |
text_to_video | happyhorse-1.0-r2v, happyhorse-1.0-t2v, happyhorse-character, happyhorse-text-to-video |
Model availability can change between releases. Use check_pricing or the HappyHorse model page for the current catalog view.
Agent Prompts
Ask your assistant in natural language; it can inspect pricing, create the task, and return the task id plus output URLs.
Create a task
Run a HappyHorse edit video task with RunAPI.
The assistant can call check_pricing, then edit_video, and return the task id, status, and output URLs.
Submit without waiting
Create the task but don't wait for it to finish.
The assistant calls the create tool with wait: false and returns the task id. Check on it later with get_task.
Check pricing before creating
Check current HappyHorse pricing, then create the task if it matches my request.
The assistant calls check_pricing and can link to the HappyHorse model page for the canonical catalog entry.
Configuration
The server resolves auth in this order:
RUNAPI_API_KEYenvironment variable, useful for headless and CI hosts~/.config/runapi/config.json, created by the MCPlogintool orrunapi login- No key, which still allows
check_pricing
The config file is normally managed by login. A pre-provisioned headless config can use:
{
"apiKey": "your_runapi_key"
}
Do not commit real API keys.
Links
| Resource | URL |
|---|---|
| HappyHorse model page | https://runapi.ai/models/happyhorse |
| npm package | @runapi.ai/happyhorse-mcp |
| GitHub repository | runapi-ai/happyhorse-mcp |
| RunAPI MCP overview | runapi.ai/mcp |
| RunAPI docs | runapi.ai/docs |
License
Licensed under the Apache License, Version 2.0.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.