V8 Cpu Profile Decoder MCP

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

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

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

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

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:

config.json

{
  "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.

config.json

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

config.json

[
  {
    "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.

config.json

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

config.json

{
  "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.

config.json

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

config.json

{
  "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.

config.json

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

config.json

{
  "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.

config.json

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

config.json

{
  "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.

config.json

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

config.json

{
  "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

Terminal

npx v8-cpu-profile-decoder-mcp

Or install globally:

Terminal

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

Generate a CPU profile in Node.js

bash

# 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

config.json

{
  "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

📄 License

MIT © vola-trebla

Read the full README →View source on GitHub →

Related MCP Servers

View all in Finance & Fintech View all alternatives

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about V8 Cpu Profile Decoder MCP

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "v8-cpu-profile-decoder-mcp": { "command": "npx", "args": ["-y", "v8-cpu-profile-decoder-mcp"] } }

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Technical Specs & Signals

CategoryFinance & Fintech

27Quality signal: Emerging · 27/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20

Documentation & tools11/30

Adoption & activity1/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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More in 💰 Finance & Fintech →Best MCP servers for Finance & Fintech →Alternatives to V8 Cpu Profile Decoder MCP →Install in Claude Desktop Install in Cursor Install in VS Code

V8 Cpu Profile Decoder MCP

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

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

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

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

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

config.json

{
  "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, ...]
}

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.

config.json

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

config.json

[
  {
    "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.

config.json

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

config.json

{
  "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.

config.json

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

config.json

{
  "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.

config.json

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

config.json

{
  "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`

config.json

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

config.json

{
  "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.

config.json

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

config.json

{
  "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

Terminal

npx v8-cpu-profile-decoder-mcp

Or install globally:

Terminal

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

Generate a CPU profile in Node.js

bash

# 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

config.json

{
  "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

📄 License

Read the full README →View source on GitHub →

Related MCP Servers

View all in Finance & Fintech View all alternatives

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about V8 Cpu Profile Decoder MCP

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "v8-cpu-profile-decoder-mcp": { "command": "npx", "args": ["-y", "v8-cpu-profile-decoder-mcp"] } }

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Technical Specs & Signals

CategoryFinance & Fintech

27Quality signal: Emerging · 27/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20

Documentation & tools11/30

Adoption & activity1/15

Community engagement0/10

★ Spotlight Slot

Feature Your MCP Server

Get maximum visibility for your server across our directory, search results, and detail pages.

Spotlight Your Server

Own this project?

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

Share & Embed

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Explore more

More in 💰 Finance & Fintech →Best MCP servers for Finance & Fintech →Alternatives to V8 Cpu Profile Decoder MCP →Install in Claude Desktop Install in Cursor Install in VS Code