Hyprland MCP Server

The Hyprland MCP Server bridges the gap between natural language models and the dynamic window‑management capabilities of the Hyprland Wayland compositor. By exposing every command through a lightweight, unofficial MCP implementation, it lets language‑model assistants—such as Claude or other AI agents—to query and manipulate the desktop environment without requiring direct shell access. This approach is especially valuable for developers building voice‑controlled workflows, accessibility tools, or AI‑augmented productivity suites that need to respond to user intent in real time.

At its core, the server provides a rich set of tools that map directly to Hyprland’s features: from listing monitors, workspaces, and client windows, to dispatching layout changes or executing arbitrary commands. Each tool returns structured JSON, enabling the AI to parse results and make informed decisions. For example, a user can ask an assistant to “switch to workspace 2” or “what is the current active window?” and the server translates these requests into precise compositor commands, returning human‑readable feedback. This eliminates the need for custom scripting or manual command line usage, making advanced desktop manipulation accessible to non‑technical users.

Key capabilities include:

• Comprehensive introspection: Tools such as , , and provide full visibility into the current desktop state, allowing AI agents to make context‑aware suggestions.
• Dynamic configuration: With and , the server can modify Hyprland’s configuration on the fly, enabling adaptive UI tweaks driven by user preferences or environmental changes.
• Safe control flow: Features like give the assistant a controlled way to close windows, reducing accidental data loss while still offering powerful automation.
• Extensibility: The tool lets developers add custom Hyprland commands without modifying the server, keeping the interface future‑proof as the compositor evolves.

Real‑world scenarios abound: a developer could build an AI‑powered window manager that automatically arranges windows based on task context, or a teaching assistant could guide new users through complex compositor settings via conversational prompts. In accessibility contexts, the server enables screen readers to query window properties or switch workspaces using natural language, improving usability for visually impaired users. For DevOps and automation pipelines, the MCP server can be scripted to set up consistent workspace layouts before a coding session or to capture window states for reproducibility.

Integration into existing AI workflows is straightforward. MCP clients—such as Claude Desktop, LangChain, or custom agents built on the Python SDK—can register the Hyprland server as a tool source. The AI then invokes tools by name, passing arguments derived from user intent parsing. Because the server returns structured JSON, downstream components can immediately consume results, trigger further actions, or feed them back into the conversation. This tight coupling between language models and desktop state makes Hyprland MCP Server a powerful enabler for intelligent, context‑aware applications on Linux.