@runapi.ai/topaz-mcp

RunAPI Topaz MCP Server

Topaz API access for AI agents: run image and 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/topaz-mcp is a focused Model Context Protocol server for the Topaz model line on RunAPI. It gives MCP-compatible assistants direct access to 2 endpoints and 2 model variants without loading the full RunAPI catalog.

Use this per-model server when an agent should stay scoped to Topaz. Use @runapi.ai/mcp when one assistant should discover every RunAPI model line.

Install

Add it to Claude Code:

claude mcp add topaz -s user -- npx -y @runapi.ai/topaz-mcp

Use project scope when the server should be shared with a repository:

claude mcp add topaz -s project -- npx -y @runapi.ai/topaz-mcp

Codex, Cursor, Windsurf, VS Code, Roo Code, and other MCP hosts can use the same stdio command:

{
  "mcpServers": {
    "topaz": {
      "command": "npx",
      "args": ["-y", "@runapi.ai/topaz-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
upscale_image	Yes	Create a Topaz upscale image task and optionally wait for a terminal status. Returns the task id, status, and output URLs.
upscale_video	Yes	Create a Topaz upscale 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 Topaz model and endpoint.

Models

Topaz covers 2 model variants across 2 endpoints. Each tool accepts the models listed for it:

Tool	Models
upscale_image	topaz-upscale-image
upscale_video	topaz-upscale-video

Model availability can change between releases. Use check_pricing or the Topaz 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 Topaz upscale image task with RunAPI.

The assistant can call check_pricing, then upscale_image, 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 Topaz pricing, then create the task if it matches my request.

The assistant calls check_pricing and can link to the Topaz model page for the canonical catalog entry.

Configuration

The server resolves auth in this order:

• RUNAPI_API_KEY environment variable, useful for headless and CI hosts
• ~/.config/runapi/config.json, created by the MCP login tool or runapi 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
Topaz model page	https://runapi.ai/models/topaz
npm package	@runapi.ai/topaz-mcp
GitHub repository	runapi-ai/topaz-mcp
RunAPI MCP overview	runapi.ai/mcp
RunAPI docs	runapi.ai/docs

License

Licensed under the Apache License, Version 2.0.