Large Language Models (LLMs) face challenges in processing the vast, complex streams of observability data, which include logs, traces, and metrics essential for system behavior analysis. The Model Context Protocol (MCP), introduced by Anthropic, addresses these challenges by standardizing how AI assistants retrieve the necessary context, regardless of the data source or LLM vendor, thus avoiding the need for multiple bespoke integrations. Groundcover's innovative MCP server transforms these raw data streams into AI-ready insights, utilizing purpose-built design choices such as log pattern summarization, drilldown mode for focusing on key attributes, and anomaly detection to provide distilled and structured insights. This approach enhances AI effectiveness by delivering curated, high-value input that aligns with AI reasoning processes, facilitated by a unique architecture combining eBPF sensors with Bring Your Own Cloud (BYOC) capabilities. As a result, AI becomes deeply integrated into observability systems, enabling developers and support teams to conduct investigations, run tests, and debug with greater efficiency and accuracy.