V8 Cpu Profile Decoder MCP server

v8-cpu-profile-decoder-mcp 🐸⚡

An MCP server that decodes V8 CPU profiles into token-efficient bottleneck summaries for AI agents.

Your Node.js app is slow. You ran --cpu-prof. Now you have a 20MB .cpuprofile file — and your AI agent is completely blind to it.

🤔 The Problem

V8 CPU profiles are massive. A typical .cpuprofile from a production Node.js app is 5–50MB of raw JSON — millions of lines mapping memory addresses, tick counts, and microsecond execution sequences. It looks like this:

{
  "nodes": [
    { "id": 1482, "callFrame": { "functionName": "processRequest", "url": "file:///app/dist/server.js", "lineNumber": 847 }, "hitCount": 3241, "children": [1483, 1490] },
    ...
  ],
  "samples": [1482, 1483, 1482, 1490, 1482, ...],
  "timeDeltas": [120, 98, 115, 102, ...]
}

An AI agent attempting to read this file instantly collapses its context window and fails. Even if it could read it, it can't run the aggregation algorithms needed to compute inclusive/exclusive CPU times across the call tree.

So when you ask your agent:

• 🙈 "Which function is consuming the most CPU?"
• 🙈 "What's calling my slow database query?"
• 🙈 "Which TypeScript file is the bottleneck actually coming from?"

...it's guessing. It has no access to the profiling data.

v8-cpu-profile-decoder-mcp fixes that. It decodes the profile locally and hands the agent a 10-line semantic summary instead of a 50MB file.

🛠️ Tools

extracthottestfunctions

Parses the .cpuprofile and returns the top N functions ranked by exclusive CPU time (self time). Filters out V8 internals and Node.js built-ins — only user code.

{
  "profile_path": "/app/profiles/CPU.20260516.cpuprofile",
  "top_n": 5,
  "min_self_percent": 1.0
}

[
  {
    "rank": 1,
    "functionName": "hashPassword",
    "url": "file:///app/dist/auth/crypto.js",
    "lineNumber": 42,
    "selfTimeMs": 1842.5,
    "totalTimeMs": 1842.5,
    "selfPercent": 61.32,
    "totalPercent": 61.32,
    "hitCount": 3241
  },
  {
    "rank": 2,
    "functionName": "parseJsonBody",
    "url": "file:///app/dist/middleware/body.js",
    "lineNumber": 18,
    "selfTimeMs": 412.1,
    "totalTimeMs": 412.1,
    "selfPercent": 13.71,
    "totalPercent": 13.71,
    "hitCount": 724
  }
]

analyzecalltree_path

Finds all callers of a specific function and shows how often each one invoked it. Accepts partial, case-insensitive function name matching.

{
  "profile_path": "/app/profiles/CPU.20260516.cpuprofile",
  "function_name": "hashPassword",
  "top_callers": 3
}

{
  "targetFunction": "hashPassword",
  "matchedNodes": 2,
  "totalSelfTimeMs": 1842.5,
  "totalPercent": 61.32,
  "callers": [
    {
      "functionName": "loginHandler",
      "url": "file:///app/dist/routes/auth.js",
      "lineNumber": 94,
      "callCount": 2180,
      "selfTimeMs": 240.1
    },
    {
      "functionName": "validateSession",
      "url": "file:///app/dist/middleware/auth.js",
      "lineNumber": 31,
      "callCount": 1061,
      "selfTimeMs": 116.8
    }
  ]
}

correlatesourcecode

Maps compiled JS bottlenecks back to their original TypeScript source locations using .js.map files. Falls back gracefully to compiled JS locations if no source map is found.

Shortened. The full README is on GitHub.