hardware_for_model — readygpu: hardware for local AI MCP Tool
hardware_for_model
(hardware for model) is one of 6 tools on the
readygpu: hardware for local AI
MCP server. Connect the server and your client discovers it on the handshake.
by client
How to call hardware_for_model from your client
- Claude Code readygpu: hardware for local AI hardware_for_model run in your project directory
- Claude Desktop readygpu: hardware for local AI hardware_for_model ~/Library/Application Support/Claude/claude_desktop_config.json
- Cursor readygpu: hardware for local AI hardware_for_model ~/.cursor/mcp.json
- VS Code readygpu: hardware for local AI hardware_for_model .vscode/mcp.json
- Zed readygpu: hardware for local AI hardware_for_model ~/.config/zed/settings.json
- Windsurf readygpu: hardware for local AI hardware_for_model ~/.codeium/windsurf/mcp_config.json
- Cline readygpu: hardware for local AI hardware_for_model ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
- Gemini CLI readygpu: hardware for local AI hardware_for_model ~/.gemini/settings.json
- Grok readygpu: hardware for local AI hardware_for_model .mcp.json (in your project root)
- ChatGPT readygpu: hardware for local AI hardware_for_model Settings → Connectors → Advanced → Developer mode
- Claude.ai readygpu: hardware for local AI hardware_for_model Settings → Connectors → Add custom connector
- LangChain readygpu: hardware for local AI hardware_for_model pip install langchain-mcp-adapters
Fastest route
claude mcp add --transport http readygpu https://readygpu.com/api/mcpOther tools on this server
This list was read from the server itself, by connecting to it and calling tools/list on 5 November 2026. It is what
the server actually exposes, not what its listing claims.
Mutating and Read-only are read off each tool's name, not its schema — a hint, not a guarantee. The registry stores tool names only; connect the server for its live schemas.