V8 Cpu Profile Decoder MCP

Name: V8 Cpu Profile Decoder MCP
Author: vola-trebla

by vola-treblaversion 0.3.0i★ –iRuns on your computer

Registry nameio.github.vola-trebla/v8-cpu-profile-decoder-mcp

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "v8-cpu-profile-decoder": {
      "command": "npx",
      "args": [
        "-y",
        "v8-cpu-profile-decoder-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add v8-cpu-profile-decoder -- npx -y v8-cpu-profile-decoder-mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "v8-cpu-profile-decoder": {
      "command": "npx",
      "args": [
        "-y",
        "v8-cpu-profile-decoder-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "v8-cpu-profile-decoder": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "v8-cpu-profile-decoder-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account vola-trebla, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 18380 servers in this category
Published to the registry more recently than 8% of the other 18380 servers in this category
Basic checks equal to or better than 100% of the other 18380 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is V8 Cpu Profile Decoder MCP safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.3.0) reached the registry 6 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Tethered Trust

Alternatives

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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

`extract_hottest_functions`

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 } ]

`analyze_call_tree_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 } ] }

`correlate_source_code`

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.

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

"resolved": [ { "rank": 1, "generatedUrl": "file:///app/dist/auth/crypto.js", "generatedLine": 42, "source": { "originalFile": "src/auth/crypto.ts", "originalLine": 38, "originalColumn": 2, "originalFunction": "hashPassword" }, "selfTimeMs": 1842.5, "selfPercent": 61.32 } ], "sourcemapErrors": [] }

`analyze_gc_pressure`

Reports garbage collection overhead as a percentage of profiling duration, broken down by GC type. Flags when GC exceeds a configurable threshold and provides a targeted recommendation.

"profile_path": "/app/profiles/CPU.cpuprofile", "threshold_percent": 10 }

"gc_ticks": 184, "total_ticks": 1240, "gc_percentage": 14.84, "gc_type_breakdown": { "scavenger": 122, "mark_sweep": 0, "mark_compact": 0, "incremental": 62, "generic": 0 }, "exceeds_threshold": true, "threshold_percent": 10, "verdict": "GC consumed 14.84% of CPU — exceeds the 10% threshold. Dominated by Scavenger (short-lived object pressure). Consider object pooling, reusing buffers, or reducing closure captures." }

`diff_profiles`

Compares two .cpuprofile files (before/after an optimization) and returns per-function CPU time deltas, normalized against each profile's total duration. Frames are matched by call-frame coordinates, not transient node IDs, so alignment is stable across profiling sessions.

"before_profile_path": "/app/profiles/before.cpuprofile", "after_profile_path": "/app/profiles/after.cpuprofile", "top_n": 5 }

"before_duration_ms": 5000, "after_duration_ms": 4800, "total_execution_delta_ms": -200, "total_execution_delta_percent": -4, "top_improvements": [ { "function_name": "hashPassword", "url": "file:///app/dist/auth/crypto.js", "line_number": 42, "before_ms": 1842.5, "after_ms": 620.1, "absolute_diff_ms": -1222.4, "relative_diff_percent": -66.34 } ], "top_regressions": [], "only_in_before": [], "only_in_after": [] }

`analyze_async_bottlenecks`

Detects event-loop overhead by identifying V8 internal frames representing async machinery — microtask queue processing, nextTick saturation, and timer/immediate callbacks.

"profile_path": "/app/profiles/CPU.cpuprofile", "threshold_percent": 10 }

"total_ticks": 1240, "async_ticks": 186, "event_loop_overhead_ms": 372, "event_loop_overhead_percent": 15.0, "dominant_async_patterns": [ { "pattern": "promise_chains", "ticks": 142, "percent": 11.45 }, { "pattern": "nexttick_saturation", "ticks": 44, "percent": 3.55 } ], "verdict": "Event-loop overhead is 15.0% of CPU — exceeds the 10% threshold. Promise chain overhead is visible in the profile. Consider batching microtasks, using Promise.all() to parallelise I/O, or offloading CPU-bound continuations to worker threads." }

🚀 Installation

npx v8-cpu-profile-decoder-mcp

Or install globally:

npm install -g v8-cpu-profile-decoder-mcp

Generate a CPU profile in Node.js

# Single run

node --cpu-prof your-script.js

With custom output dir

node --cpu-prof --cpu-prof-dir ./profiles your-script.js

Or programmatically via Chrome DevTools → Performance tab → Record.

Claude Desktop config

"mcpServers": { "v8-cpu-profile-decoder-mcp": { "command": "npx", "args": ["-y", "v8-cpu-profile-decoder-mcp"] } } }

💡 Example Agent Prompts

_"Here's my CPU profile at /app/profiles/CPU.cpuprofile — which function is consuming the most CPU?"_

_"Find what's calling processRequest in this profile and how often"_

_"Map the top 10 hottest functions back to their original TypeScript files"_

_"My Node.js API is slow under load — profile is at /tmp/CPU.cpuprofile, find the bottleneck"_

_"Is GC the bottleneck? Check the profile at /tmp/CPU.cpuprofile and tell me what kind of allocation is causing it"_

_"Compare these two profiles before and after my optimization — which functions improved and which regressed?"_

_"Is this app spending too much CPU on async overhead and event-loop machinery?"_

🔗 Related Projects

playwright-trace-decoder-mcp — decode Playwright traces for CI failure root-cause analysis
playwright-network-chaos-mcp — simulate network failures and latency in browser sessions
flakiness-knowledge-graph-mcp — knowledge graph of flaky test patterns
ast-impact-mapper-mcp — find affected tests from code changes via TypeScript AST
playwright-spatial-layout-mcp — geometric spatial awareness of web layouts

V8 Cpu Profile Decoder MCP

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

Tethered Trust

README from the maker

v8-cpu-profile-decoder-mcp 🐸⚡

🤔 The Problem

🛠️ Tools

extract_hottest_functions

analyze_call_tree_path

correlate_source_code

analyze_gc_pressure

diff_profiles

analyze_async_bottlenecks

🚀 Installation

Generate a CPU profile in Node.js

With custom output dir

Claude Desktop config

💡 Example Agent Prompts

🔗 Related Projects

📄 License

`extract_hottest_functions`

`analyze_call_tree_path`

`correlate_source_code`

`analyze_gc_pressure`

`diff_profiles`

`analyze_async_bottlenecks`