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v8-cpu-profile-decoder-mcp MCP Server

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

Publisher claimed. No tool list reported, and Pod has not connected to this server.

Status

Pod has not dialled v8-cpu-profile-decoder-mcp yet, so everything on this page is what its publisher reported rather than what we observed. Registries describe servers; they do not connect to them. Until a check runs, treat the tool list below as a claim.

Connect

Published as v8-cpu-profile-decoder-mcp on npm. Runs locally.

Known issues

5 problems reported by people outside the maintainer team. Issues filed by the project's own maintainers are excluded.

Most discussed

feat: add analyze_async_bottlenecks tool

Problem

Functions that await I/O appear as idle frames in CPU profiles — invisible to the agent. But Promise orchestration overhead IS captured as specific V8 C++ internal frames. When the agent only sees synchronous hotspots, it optimizes the wrong code entirely.

Signal

High self-time in these specific V8 internal frames indicates microtask queue saturation:

Frame Meaning
v8::internal::MicrotaskQueue::RunMicrotasks Promise queue being drained continuously
`

Read the thread · 2026-05-18 · closed · 0 comments

feat: add diff_profiles tool

Problem

Comparing before/after optimization is the most common profiling workflow, but the agent analyses each profile in isolation. Raw absolute timing deltas are misleading without normalization — identical code shows different raw times across two sampling periods of different lengths.

What the agent gains

Absolute and relative deltas per call frame — immediately sees "computeHash() improved by 2.24s (-22.3%)" without manual calculation. Improvements and regressions separated. Agen

Read the thread · 2026-05-18 · closed · 0 comments

fix: correlate_source_code must resolve TypeScript source maps

Problem

.cpuprofile call frames reference compiled JavaScript: scriptId, lineNumber, columnNumber all point to emitted JS. Current correlate_source_code likely returns these compiled coordinates directly — the agent sees dist/bundle.js:1:4821 instead of src/services/hash.ts:42.

Unlike Error stack traces (which can be auto-mapped via source-map-support), sample-based .cpuprofile files are generated natively by V8 in C++ and do not automatically traverse source maps.

Read the thread · 2026-05-18 · closed · 0 comments

feat: enhance flame graph summarization (framework collapse, recursive aggregation)

Problem

Deep call stacks (100+ frames) from extract_hottest_functions flood the agent's context window with noise. Express routing chains, V8 built-in frames, and deep recursive calls dilute the actionable signal. The agent can't see the user-land hotspot through the framework scaffolding.

What the agent gains

Condensed, high-signal output focused on user-land code. Same diagnostic value, fraction of the tokens.

Three summarization layers

1. Framework collapsing Consecutive f

Read the thread · 2026-05-18 · closed · 0 comments

feat: add analyze_gc_pressure tool

Problem

Garbage collection pauses are a primary Node.js latency source, but the server never surfaces them. V8 profiles capture GC as (garbage collector) nodes and deeper C++ internal frames (ScavengeVisitor::VisitPointers, SweepSpace) — these are currently invisible to the agent.

The agent can't diagnose memory allocation thrashing and instead wastes time optimizing synchronous hotspots that aren't the real bottleneck.

What the agent gains

GC time as % of total execution, broken

Read the thread · 2026-05-18 · closed · 0 comments

Firsthand observations

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For agents

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