WhoAmI MCP Server

Overview

The WhoAmI MCP Server is a minimal yet powerful tool designed to bridge the gap between an AI assistant and the underlying operating system. It solves a common developer pain point: reliably identifying the current user context without exposing sensitive environment details or requiring complex authentication flows. By simply invoking a single, lightweight tool, developers can embed the local system’s username into AI-driven workflows, enabling personalized interactions and context-aware decision making.

At its core, the server exposes a single synchronous tool named . When called, it returns the username of the user running the process. This straightforward capability is surprisingly valuable in scenarios where an AI assistant must tailor responses, enforce access controls, or log actions with a clear user identifier. Because the tool runs locally and executes instantly, it introduces negligible latency—making it ideal for integration with local large language models (LLMs) or on‑premise deployments where network round trips are undesirable.

Key features include:

• Synchronous, low‑latency execution: Perfect for real‑time AI interactions without the overhead of asynchronous polling or webhooks.
• Zero external dependencies: Written in Python 3.10+, it requires only the standard library, ensuring compatibility across most development environments.
• Easy integration with Smithery: The server is pre‑registered on the Smithery platform, allowing quick installation into Claude Desktop or other MCP‑enabled clients with a single command.

Typical use cases span from simple “Hello, <user>” greetings in chatbots to more complex security workflows where the assistant must verify that the current session belongs to an authorized user before executing privileged commands. In data‑processing pipelines, the tool can tag logs or outputs with the originating user’s name, facilitating audit trails and debugging.

By providing a reliable source of identity information, the WhoAmI MCP Server enhances developer productivity and AI reliability. Its minimal footprint, coupled with seamless integration into existing MCP workflows, makes it a standout choice for any project that needs to reconcile AI logic with the real‑world user context.