v8-cpu-profile-decoder-mcp

An MCP server that decodes V8 CPU profiles intotoken-efficient bottleneck summariesfor AI agents.

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

V8 CPU profiles are massive. A typical.cpuprofilefrom a production Node.js app is5–50MB 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 fileinstantly collapses its context windowand 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.

- 🙈"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-mcpfixes that. It decodes the profile locally and hands the agent a10-line semantic summaryinstead of a 50MB file.

Parses the.cpuprofileand 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 } ]

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 } ] }

Maps compiled JS bottlenecks back to theiroriginal TypeScript source locationsusing.js.mapfiles. 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": [] }

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." }

Compares two.cpuprofilefiles (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": [] }

Detects event-loop overhead by identifying V8 internal frames representing async machinery — microtask queue processing,nextTicksaturation, 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." }

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

# 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.

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

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

"Find what's callingprocessRequestin 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.cpuprofileand 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?"

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