
React Render Profile Mcp
io.github.vola-trebla由 GitHub 账号 vola-trebla 发布v1.0.2更新于 Nov 3, 2026
Decodes React DevTools Profiler exports into render summaries and memoization advice for AI agents.
概览
把 React DevTools Profiler 导出文件解析为渲染摘要、memo 化建议,并可为 AI 编码代理直接改写组件源码。
- 可以做什么
- 汇总性能剖析:提交次数、总渲染耗时、无效渲染与生命周期异常
- 找出浪费的渲染并归类触发原因,评估 React Compiler 或 React.memo 被绕过的位置
- 诊断水合不匹配、Suspense 瀑布、外部 store 选择器不稳定及状态级联深度
- 给出高回报的 memo 化建议,并静态审计组件源码的编译器规则违规
- 直接改写磁盘上的组件源码:提升字面量、包裹 hooks、套用 React.memo
- 检查 RSC Flight 负载的分块大小、瀑布与已知漏洞,并把提交与 Chrome trace 对齐估算 CLS/INP
- 适用场景
- 适合在代理重构 React 组件、Context Provider 或 store 选择器时,用真实运行时渲染数据代替猜测;也适合排查卡顿、输入延迟或重复渲染风暴,前提是能导出 React DevTools 性能剖析文件。
- 运行要求
- 以 stdio 在本地运行,通常通过 npx 启动,因此需要 Node.js。所有剖析工具都需要 React DevTools Profiler 导出的 JSON 文件的绝对路径;修复与审计工具需要源码文件路径。未声明任何账号、API 密钥或环境变量。
安装
在 SourceWeft 中
- 打开 控制台中的 React Render Profile Mcp,将其添加到工作区。
- 为需要使用其工具的对话启用该服务。
Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。
其他 MCP 客户端
参照 仓库 中的启动说明。
README
react-render-profile-mcp
[npm version] [npm downloads] [CI] [License: MIT]
An Autonomous Performance SRE & Auto-Remediation Engine for React, exposed as a Model Context Protocol (MCP) server. Built specifically to bridge the performance perception gap for AI coding agents (Claude, Cursor, Copilot).
👁️ The Blind Spot: Why AI Agents Break React Production
When an AI agent refactors a Context Provider, changes state architecture, or hooks up a global store, it is completely blind to the runtime performance impact.
An agent can successfully pass unit tests and compile code while introducing catastrophic performance regressions:
- A single state update cascading into 80 unnecessary child re-renders.
- Infinite rendering loops triggered by unstable Zustand/Redux selector references.
- Hydration mismatches forcing React to throw away server-rendered HTML and mount from scratch.
- Unstable
keyprops causing components to unmount and mount on every render cycle (lifecycle anomalies).
react-render-profile-mcp gives AI agents a "third eye" to dynamically measure, visualize, and auto-remediate these performance bottlenecks.
⚡ The Four Pillars of AI-SRE
Instead of raw JSON dumps, this server organizes performance data into structured, actionable insights across four core engineering pillars:
🧠 1. AST Auto-Remediation Engine (ts-morph)
ア When a bottleneck is found, the agent doesn't need to manually rewrite code. The server can mutate component source code on disk:
- Hoisting: Statically hoisting object and array literals out of render bodies.
- Dynamic Memoization: Wrapping unstable functions and variables in
useCallbackanduseMemowith computed dependency arrays. - ROI Wrapping: Wrapping components in
React.memoonly if the profiled self-time and spurious render count justify the comparison overhead (ROI > 1.5). - Rule Auditing: Scanning code to prevent compiler bailouts (detecting
Date.now(),Math.random(), or render-phaseuseRefmutations).
📊 2. Interactive SVG Cascade Visualizer (MCP Resource)
Generates parent-child rendering graphs directly into the agent's chat window using the custom resource URI scheme react-profile://commits/{commitId}/cascade?profile_path={profile_path}.
- Triggers are styled with distinct HSL palettes to isolate propagation channels:
- 🔵 Context Trigger: Blue (ocean wave)
- 🟠 Zustand/Redux Store: Orange (subscriber ripple)
- 🔴 Props Invalidation: Red (reference mismatch)
- 🟢 State Change: Green (emerald trigger source)
🛡️ 3. RSC Flight Stream & Security Profiler
Analyzes React Server Components (RSC) Flight streams to optimize delivery:
- Identifies bloated chunks (> 50KB) and sequential waterfalls.
- Scans payloads for prototype traversal vulnerabilities like CVE-2025-55182 (React2Shell) exploits.
⚛️ 4. Multi-Layer Trace Correlator
Aligns React commits with Chrome Performance timeline events using blink.user_timing markers.
- Measures post-commit layout, paint, and style calculation tasks to calculate real Core Web Vitals impacts (CLS / INP estimates).
🛠️ MCP Tools Reference
Below is a compact summary of the tools exposed by this server. All tools accept a required profile_path pointing to a React DevTools export .json.
🤖 Prompt Injection: Teach Your Agent to Profile
To get the most out of this server, add the following prompt to your agent's system instructions (e.g., in .cursorrules, Cursor System Prompt, or Claude Custom Instructions):
📋 How to Export a Profile
- Open React DevTools in your browser.
- Navigate to the Profiler tab.
- Click the Record button (circle), interact with your application to trigger the performance issue, and click Stop.
- Click the Save Profile icon (💾) to download the
.jsonfile. - Provide the absolute path to this file to the MCP server.
⚙️ Setup & Installation
Claude Desktop
Add this to your claude_desktop_config.json:
Cursor / VS Code / Other Clients
Add an MCP server of type command:
- Command:
npx -y react-render-profile-mcp
🔧 Under the Hood
- ESM-Native: Built with TypeScript ESM, optimized for fast Node.js imports.
- React DevTools v5 Protocol: Natively decodes serialized operations arrays, resolving fiber snapshots and name maps.
- Lane Identification: Distinguishes between high-priority lane updates and concurrent transition commits (Low Priority/Idle) to prevent false-positive regression flags.
- AST Modification Safety: Implements
ts-morphstatement manipulation blocks, avoiding common parser state corruption during multi-pass rewrites.
Part of the MCP Toolbelt
Developed alongside:
- tailwind-context-resolver-mcp — Resolve Tailwind design tokens and validate utility classes.
- v8-cpu-profile-decoder-mcp — Decode V8 CPU profiles for Node.js backend performance tuning.
License
MIT
来源:README.md,提交 e834c0d
工具
0版本历史
1- v1.0.2最新Sep 16, 2026
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