perf-mcp
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- AlicenseAqualityDmaintenanceHelps 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.4Apache 2.0
- FlicenseNot gradedqualityAmaintenanceEnables coding agents to trace, profile, and investigate performance hotspots in application code, native code, GPU kernels, and inference stacks by coordinating local profilers, benchmark tools, trace processors, and direct capture targets. Returns bounded, typed runtime evidence with observed inputs, provenance, coverage, and limitations, all through explicit artifact paths or commands with no workspace setup required.-
- FlicenseNot gradedqualityDmaintenanceEnables LLMs to safely write and run bpftrace scripts against the Linux kernel for observability, with explicit probe allowlists and execution timeout.1-
- FlicenseNot gradedqualityAmaintenanceEnables coding agents to coordinate profilers, benchmark tools, and trace processors to turn explicit artifacts or commands into bounded runtime evidence with typed provenance, coverage, and limitations. Provides named analysis and capture tools for tracing and profiling hotspots across application code, native code, GPU kernels, and inference stacks.-
- AlicenseNot gradedqualityDmaintenanceEnables 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.9MIT
- FlicenseNot gradedqualityDmaintenanceWraps Intel VTune Profiler CLI as MCP tools, enabling Claude Code to analyze performance data such as hotspots, summaries, and comparisons via natural language.1-
TDQS
Scored across 26 tools
Each tool wraps a distinct perf subcommand with a unique analysis purpose, from report and annotate to sched-specific and kwork-specific views. Even similar tools like perf_sched_latency vs perf_sched_timehist are clearly differentiated by their descriptions.
All tools follow the exact perf_<subcommand> pattern, with no mixing of casing or verb styles. The naming is uniform and predictable, making the toolset easy to navigate.
At 26 tools, the server exceeds the 25-tool threshold for 'too many'. While perf is a broad domain, this many tools risks overwhelming an agent, and several are niche or could be consolidated.
The toolset is heavily analysis-focused but completely lacks capture tools like perf_record or perf_stat, making it impossible to generate the very perf.data files most tools require. This is a significant gap that forces the agent to depend on external data sources.