Use this tool when a user pastes `perf report --stdio` output and wants to know
which functions are consuming the most CPU cycles on their Arm64 system.

Args:
    perf_report_text: Raw stdout of `perf report --stdio` or similar.
    top_n: Maximum number of hot symbols to return (default 10).
    min_overhead_pct: Filter out symbols below this overhead percentage (default 0.5).

Returns:
    {
      "summary": {"total_samples": int, "total_events": int | None, "command": str | None},
      "hot_symbols": [
        {"overhead_pct": 12.34, "samples": 1234, "command": "...",
         "module": "...", "symbol": "..."},
        ...
      ],
      "warnings": [...]
    }

Example: analyze_perf_output(perf_report_text=text, top_n=5, min_overhead_pct=1.0)
returns the 5 hottest symbols above 1% overhead.

Use this tool when a user asks which NEON intrinsic to use for a hot loop, wants to
vectorize an operation on Arm64, or mentions intrinsic names like vmlaq_f32.

Args:
    operation_description: Natural-language description of the operation
        (e.g., "vectorized 32-bit float multiply-accumulate" or "vmlaq_f32").
    target_arch: Target Arm architecture — "armv8-a" (default), "armv8.2-a", or "armv9-a".
    top_k: Number of suggestions to return (default 5).

Returns:
    {
      "matches": [
        {"intrinsic": "vmlaq_f32", "signature": "...", "header": "<arm_neon.h>",
         "min_arch": "armv8-a", "description": "...", "score": 0.83},
        ...
      ],
      "notes": "Filtered to armv8-a. KB contains N entries (M compatible)."
    }

Example: suggest_neon_intrinsic("32-bit float fused multiply-add") returns vfmaq_f32 at rank 1.

Use this tool when a user wants to know whether their Python dependencies
will install on an Arm64 server (AWS Graviton, Google Axion, Ampere Altra,
Raspberry Pi 5), or when they ask why a package fails on arm64.

Args:
    file_content: Raw text of the dependency manifest.
    file_type: One of "requirements.txt", "pyproject.toml", or "Dockerfile".

Returns:
    {
      "checked": ["numpy", "tensorflow", ...],
      "issues": [
        {"package": "tensorflow", "severity": "warning",
         "message": "Official TensorFlow PyPI wheels are x86-only..."}
      ],
      "summary": "Checked 5 package(s): 1 warning(s)."
    }

Severity levels: "error" = no arm64 wheel at all (e.g. GPU-only packages);
"warning" = arm64 wheel exists but requires workaround;
"info" = wheel available but version constraint or system lib note applies.

Example: check_arm64_deps(content, "requirements.txt") flags cupy (error),
tensorflow (warning), and reports numpy arm64 wheel availability (info).