io.github.vola-trebla/v8-cpu-profile-decoder-mcp
MCP server that decodes V8 CPU profiles into token-efficient bottleneck summaries for AI agents
Open source Open in the app JSON README (API)
About
MCP server that decodes V8 CPU profiles into token-efficient bottleneck summaries for AI agents
Details
- Kind
- MCP servers
- Topic
- No topic detected
- Publisher
- vola-trebla
- Origin
- official
- Category
- ferramentas
- Transport
- local
- Version
- 0.3.0
- Stars
- 1
- Last push
- 2026-05-19T22:07:32Z
- Repository state
- ativo
- Language
- TypeScript
- License
- MIT
- Added
- 2026-08-29 04:01:39
- Updated
- 2026-08-29 04:01:39
- Origin id
io.github.vola-trebla/v8-cpu-profile-decoder-mcp
README
# v8-cpu-profile-decoder-mcp 🐸⚡
[](https://www.npmjs.com/package/v8-cpu-profile-decoder-mcp)
[](https://www.npmjs.com/package/v8-cpu-profile-decoder-mcp)
[](https://github.com/vola-trebla/v8-cpu-profile-decoder-mcp/actions/workflows/ci.yml)
[](https://opensource.org/licenses/MIT)
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:
```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.
```json
{
"profile_path": "/app/profiles/CPU.20260516.cpuprofile",
"top_n": 5,
"min_self_percent": 1.0
}
```
```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.
```json
{
"profile_path": "/app/profiles/CPU.20260516.cpuprofile",
"function_name": "hashPassword",
"top_callers": 3
}
```
```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.
```json
{
"profile_path": "/app/profiles/CPU.20260516.cpuprofile",
"top_n": 5
}
```
```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.
```json
{
"profile_path": "/app/profiles/CPU.cpuprofile",
"threshold_percent": 10
}
```
```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.
```json
{
"before_profile_path": "/app/profiles/before.cpuprofile",
"after_profile_path": "/app/profiles/after.cpuprofile",
"top_n": 5
}
```
```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.
```json
{
"profile_path": "/app/profiles/CPU.cpuprofile",
"threshold_percent": 10
}
```
```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
```bash
npx v8-cpu-profile-decoder-mcp
```
Or install globally:
```bash
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
```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](https://github.com/vola-trebla/playwright-trace-decoder-mcp) — decode Playwright traces for CI failure root-cause analysis
- [playwright-network-chaos-mcp](https://github.com/vola-trebla/playwright-network-chaos-mcp) — simulate network failures and latency in browser sessions
- [flakiness-knowledge-graph-mcp](https://github.com/vola-trebla/flakiness-knowledge-graph-mcp) — knowledge graph of flaky test patterns
- [ast-impact-mapper-mcp](https://github.com/vola-trebla/ast-impact-mapper-mcp) — find affected tests from code changes via TypeScript AST
- [playwright-spatial-layout-mcp](https://github.com/vola-trebla/playwright-spatial-layout-mcp) — geometric spatial awareness of web layouts
---
## 📄 License
MIT © [vola-trebla](https://github.com/vola-trebla)