Get Installed Apps MCP Server

The Get Installed Apps MCP Server solves a common pain point for developers building AI‑augmented workflows: the need to programmatically discover what software is present on a user’s machine. By exposing this information through the Model Context Protocol, the server allows AI assistants—such as Claude, Raycast, or Cursor—to query the local environment and adapt their behavior accordingly. For example, a conversational agent can ask whether a particular IDE is available before suggesting code‑generation tips or can trigger a file search only if the relevant application exists.

At its core, the server implements a single MCP tool named Get Installed Apps. When invoked, it scans the operating system (MacOS or Windows) and returns a structured JSON payload listing every installed application. The response is wrapped in plain text so that the client can parse it easily, and any errors are communicated with a clear error message. Because the tool requires no parameters, it is trivial to call from within an AI prompt: a user can simply request the list and immediately receive actionable data.

Key capabilities of this server include:

• Cross‑platform support: works on both MacOS and Windows without additional configuration.
• Lightweight operation: minimal dependencies mean the server can run in constrained environments or be embedded into larger toolchains.
• Seamless MCP integration: follows the MCP specification, so any compliant client can discover and invoke the tool without custom adapters.

Typical use cases span from developer productivity to automation:

• Environment validation: an AI helper can confirm that required tools (e.g., Git, Docker) are installed before proceeding with setup instructions.
• Dynamic menu generation: a desktop assistant can populate context menus with available applications, allowing quick launches directly from the chat interface.
• Conditional workflows: scripts or pipelines can branch based on the presence of specific software, reducing manual checks.

The server’s design gives it a distinct advantage in AI workflows: by turning the local application inventory into an API‑exposed resource, it removes a barrier to building truly context‑aware assistants. Developers can now write prompts that ask “What apps do I have?” and receive a reliable, machine‑readable answer—enabling richer interactions, smarter recommendations, and tighter integration between human intent and system capabilities.