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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 4 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.


Related MCP server: GPT Image MCP Server

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, output URLs, and pricing snapshot.

image_to_video

Yes

Create a HappyHorse image to video task and optionally wait for a terminal status. Returns the task id, status, output URLs, and pricing snapshot.

text_to_video

Yes

Create a HappyHorse text to video task and optionally wait for a terminal status. Returns the task id, status, output URLs, and pricing snapshot.

get_task

Yes

Fetch the current status and latest payload for an existing task.

check_pricing

No

Look up the current pricing snapshot for a HappyHorse model and endpoint.


Models

HappyHorse covers 4 model variants across 3 endpoints. Each tool accepts the models listed for it:

Tool

Models

edit_video

happyhorse-edit-video

image_to_video

happyhorse-image-to-video

text_to_video

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:

  1. RUNAPI_API_KEY environment variable, useful for headless and CI hosts

  2. ~/.config/runapi/config.json, created by the MCP login tool or runapi login

  3. 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.


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.

Install Server
A
license - permissive license
A
quality
A
maintenance

Maintenance

Maintainers
Response time
2dRelease cycle
7Releases (12mo)
Commit activity

Resources

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