Osquery MCP Server

Overview

The Osquery MCP Server is a purpose‑built bridge that lets AI assistants, such as Claude, interrogate the underlying operating system through Osquery, a powerful SQL‑based introspection tool. By exposing a Model Context Protocol (MCP) interface, the server translates natural‑language questions into precise Osquery queries and returns structured results that can be consumed by any MCP‑compatible client. This eliminates the need for developers to write custom parsers or maintain separate diagnostic scripts, allowing AI models to deliver real‑time system insights directly within a conversational context.

At its core, the server implements nine specialized tools tailored to common diagnostic scenarios: executing arbitrary Osquery SQL, retrieving table schemas, identifying high CPU or memory consumers, analyzing network connections, monitoring hardware temperatures and fan speeds (on macOS), generating a comprehensive health summary, and providing example queries for typical problems. Each tool is designed to be self‑contained yet composable, so an AI assistant can chain them together or invoke the most appropriate one based on the user’s intent. The inclusion of schema discovery and example queries means that even non‑technical users can ask complex questions—such as “Which process is using the most disk I/O?”—and receive accurate, actionable answers without writing SQL themselves.

The server’s architecture is built on Spring Boot 3.5 with Java 21, ensuring modern performance and maintainability while leveraging familiar enterprise patterns. It runs in STDIO mode, making it trivially compatible with Claude Desktop and any other tool that speaks MCP over standard input/output. Robust error handling, query timeouts (30 seconds for queries, 5 seconds for version checks), and automatic process cleanup guarantee that the server remains responsive even under heavy load or malformed queries.

A companion Spring AI MCP client demonstrates how to consume the server programmatically or via an interactive REPL. The client uses Spring AI’s auto‑configuration to discover available tools automatically, supports natural language routing, and exposes a simple CLI for exploratory diagnostics. This client can be dropped into existing Spring applications or run standalone, providing a ready‑made example of integrating MCP into an AI workflow.

In practice, the Osquery MCP Server empowers a range of real‑world use cases: DevOps teams can embed live system diagnostics into chatbots that answer support tickets; security analysts can query for anomalous processes or open network ports on demand; and end‑user applications can surface hardware health metrics in conversational interfaces. By abstracting the complexity of Osquery behind MCP, developers can focus on crafting richer AI experiences while still having deep, programmatic access to the underlying system.