Helps AI assistants optimize Linux workloads on Arm64 by parsing perf report output, recommending NEON SIMD intrinsics for hot loops, and auditing Python dependency manifests for arm64 wheel availability — all offline and structured.
Enables LLMs to safely write and run bpftrace scripts against the Linux kernel for observability, with explicit probe allowlists and execution timeout.
Enables LLMs to perform high-performance code search and analysis across multiple languages using symbol indexing, regex text search, and structural AST pattern matching. It also provides tools for technology stack detection and dependency analysis with persistent caching for optimized performance.
Wraps Intel VTune Profiler CLI as MCP tools, enabling Claude Code to analyze performance data such as hotspots, summaries, and comparisons via natural language.
Provides typed, paginated tools for evidence-driven Linux performance analysis including profile analysis, comparison, and report generation with server-side authorization.
