The Buildkite MCP server project focuses on enhancing log fetching, parsing, and querying to aid AI agents in analyzing CI job logs effectively. Initially, the MCP server used Buildkite's public REST API to return job logs, but large and complex logs posed challenges for AI agents, often leading to incomplete or inaccurate analysis of build failures. To address this, a preprocessing step was introduced to convert raw logs into a structured, line-oriented format, stored in Parquet files for efficient access and filtering. This structured approach, combined with log-navigation tools such as tail_logs, search_logs, read_logs, and get_logs_info, enables AI agents to follow a human-like debugging workflow by identifying failures, exploring relevant log segments, and summarizing findings. The project highlights the importance of providing AI agents with streamlined and clear tools to enhance their effectiveness in analyzing CI systems, encouraging community engagement and contributions to further improve the MCP server.