Personal Productivity Agent

The Personal Productivity Agent is a versatile Python‑based service that brings everyday Windows productivity tasks into the hands of large language models (LLMs). By exposing a rich set of file‑system, system‑information, and productivity utilities over the Multi‑Capability Protocol (MCP), it turns a local machine into an intelligent assistant that can be orchestrated by any AI client—whether that’s Claude, a custom chatbot, or an automated workflow engine. The core idea is to let LLMs reason about system events and then execute precise, safe actions on the host machine.

At its heart, the agent solves a common pain point for developers and power users: bridging the gap between natural‑language intent and low‑level Windows operations. Instead of manually opening PowerShell, typing commands, or hunting for files, a user can ask the LLM to “find all duplicate images in my Pictures folder” or “summarize the latest meeting notes.” The agent interprets these requests, performs the required file‑system or system queries, and returns structured results. When an LLM is involved—such as Google Gemini for event‑log analysis—the agent can also provide contextual explanations, suggest troubleshooting steps, and even generate a command line that resolves the issue while indicating whether administrative rights are needed.

Key capabilities include:

• File‑system manipulation: Search, move, rename, and create directories with confirmation safeguards.
• System diagnostics: Retrieve disk usage, memory, CPU, and network statistics; launch applications; locate duplicate files.
• Productivity helpers: Summarize documents, pull upcoming calendar events from files, and set desktop reminders.
• Event‑log intelligence: Pull Windows Event Logs based on filters and feed them to Gemini, which returns a human‑readable analysis, potential causes, and an optional corrective command.

These features are exposed through two operational modes. In interactive CLI mode, the agent offers a menu‑driven interface for direct use, while in MCP server mode it listens on standard input/output for JSON messages. This design allows external orchestrators—such as a TypeScript‑based agent or an automation hub—to invoke any capability programmatically, enabling complex workflows that combine LLM reasoning with automated system actions.

For developers, the standout advantage is the modular, JSON‑based communication. Each tool can be treated as a discrete service; an orchestrator can compose them into higher‑level agents without reimplementing logic. Because the agent runs locally, latency is minimal and privacy is preserved—data never leaves the host machine. Whether you’re building a personal assistant, automating IT support tasks, or creating a smart maintenance agent for Windows environments, the Personal Productivity Agent provides a robust foundation that unites LLM insight with reliable system control.