react-profiler-mcp
Streams live React profiler data to AI agents, enabling them to analyze performance and suggest specific, actionable fixes for slow components.
README
<div align="center">
react-profiler-mcp
Give your AI agent real React performance data. Get specific fixes, not guesses.
<br />
"Which components are making my app slow, and how do I fix them?"
— a question your AI agent can now answer with real data
</div>
The problem
React DevTools Profiler gives you raw render timings. But staring at a flame graph and knowing exactly what to change in your code are two different things. Most developers either ignore the data or spend hours guessing.
react-profiler-mcp streams live profiler data from your app directly to your AI agent — so instead of a flame graph you can't read, you get this:
ProductList re-renders 47× on this page, averaging 68ms per render.
The root cause is onAddToCart being recreated on every parent render,
which breaks React.memo() on the child. Fix:
const onAddToCart = useCallback((id) => {
dispatch({ type: 'ADD', id });
}, [dispatch]);
That alone should drop renders from 47 to 3.
How it works
Your React app AI agent
───────────────── ────────────────────────
<ProfilerBridge> → POST → HTTP ingest (:8787)
records every │
render commit in-memory store
│
MCP stdio server
│
"analyze_performance" tool
│
Cursor / Claude / Copilot
reads real samples and
gives you targeted fixes
One local Node process handles both sides. The browser POSTs render samples over loopback; your editor connects to the same process over MCP stdio.
Works with any MCP-compatible agent
| Editor / Agent | Setup |
|---|---|
| Cursor | Add to .cursor/mcp.json |
| Claude Desktop | Add to claude_desktop_config.json |
| Windsurf | Add to MCP settings |
| GitHub Copilot (VS Code) | Add to .vscode/mcp.json |
| Gemini CLI | Add to ~/.gemini/settings.json |
| Any MCP client | Same config, same server |
Quickstart
1. Install the collector
npm install @react-profiler-mcp/react-collector
2. Wrap your app
import { ProfilerBridge } from '@react-profiler-mcp/react-collector';
export function App() {
return (
<ProfilerBridge
ingestUrl="http://127.0.0.1:8787/v1/profile-samples"
sessionId="my-app"
profilerId="main-shell"
>
<YourApp />
</ProfilerBridge>
);
}
3. Add the MCP server to your editor
Cursor / Windsurf / VS Code — add to your project's .cursor/mcp.json or .vscode/mcp.json:
{
"mcpServers": {
"react-profiler": {
"command": "npx",
"args": ["-y", "@react-profiler-mcp/mcp-server"]
}
}
}
<details> <summary>Claude Desktop</summary>
~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or
%APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": {
"react-profiler": {
"command": "npx",
"args": ["-y", "@react-profiler-mcp/mcp-server"]
}
}
}
</details>
<details> <summary>Gemini CLI</summary>
~/.gemini/settings.json:
{
"mcpServers": {
"react-profiler": {
"command": "npx",
"args": ["-y", "@react-profiler-mcp/mcp-server"]
}
}
}
</details>
4. Use your app, then ask
Open your React app and interact with it normally for 30–60 seconds. Then in your AI agent's chat:
Analyze my React app's performance. Which components are
the worst offenders and what exactly should I change?
The agent calls the profiler tools, reads the real samples from your session, and gives you specific fixes — component names, line-level suggestions, and why each change helps.
Next.js
ProfilerBridge uses client hooks. Keep it inside a Client Component:
'use client';
import { ProfilerBridge } from '@react-profiler-mcp/react-collector';
const ingestUrl =
process.env.NEXT_PUBLIC_PROFILER_INGEST_URL ?? 'http://127.0.0.1:8787/v1/profile-samples';
export default function Page() {
return (
<ProfilerBridge ingestUrl={ingestUrl} sessionId="my-next-app" profilerId="page-root">
{/* page content */}
</ProfilerBridge>
);
}
Add to next.config.js:
const nextConfig = {
transpilePackages: ['@react-profiler-mcp/react-collector'],
};
export default nextConfig;
ProfilerBridge props
| Prop | Type | Default | Description |
|---|---|---|---|
ingestUrl |
string |
— | Required. Full URL to POST samples to. |
sessionId |
string |
(omit) | If set, sent as X-React-Profiler-Session. If omitted, the server stores samples under default — match that in MCP tools like session_summary, or set a custom id here and pass the same string as sessionId in tools. |
profilerId |
string |
"react-profiler-mcp-root" |
React <Profiler id={...}>. Becomes componentName in ingest/MCP. Prefer distinct ids per subtree you care about ("checkout-form", "data-table"). |
enabled |
boolean |
true |
Set false to disable profiling and HTTP traffic (e.g. production). |
MCP tools
The agent can call these tools once connected (names match the server in packages/mcp-server/src/mcp/server.ts):
| Tool | What it returns |
|---|---|
list_sessions |
All ingest buckets: sessionId, counts, timestamps |
get_profiler_data |
Raw sample tail (ids, phases, durations); optional sessionId defaults to most recently updated session |
get_component_summary |
Per Profiler id (profilerId) stats, sortable |
get_slow_renders |
Renders over thresholdMs (default 16ms) |
analyze_performance |
Composite report: offenders, re-renders, heuristic suggestions |
clear_data |
Clears all in-memory sessions |
session_summary |
Aggregate stats for one session (sessionId defaults to default if omitted) |
list_recent_samples |
Recent raw rows for citations |
explain_jank |
Heuristic jank signals over a time window |
suggest_fixes |
Ranked remediation ideas tied to captured labels |
⚠️ One process rule
Run either a manually started server or the one your editor spawns — not both. Two processes means two separate in-memory stores. MCP will connect to one; the browser posts to the other. Data never meets.
stdio MCP: Your editor usually spawns the server (npx / node …) and owns stdin/stdout for the protocol. You generally do not attach MCP to a server you already started in a separate terminal (that process’s stdio is tied to the shell). For logs, rely on stderr from the editor-spawned process, or run a second terminal only for HTTP debugging (accepting that MCP in the editor will use a different store unless you use a single process — see docs/MCP_USAGE.md).
Quick check — the process logs the ingest URL on stderr (stdout is reserved for MCP when an editor spawns it):
npx -y @react-profiler-mcp/mcp-server
Notes
- Production: React’s
<Profiler>still runsonRenderin normal production builds (with some overhead). For shipped apps, setenabled={false}(or omitProfilerBridge) unless you deliberately want field metrics. - In-memory store. Data resets when the server restarts. This is intentional — it's a dev tool, not a database.
- Loopback only. The ingest listener binds to
127.0.0.1(seepackages/mcp-server/src/index.ts). Override port with envPORT; do not expose the port publicly.
Packages
| Package | npm | Description |
|---|---|---|
@react-profiler-mcp/react-collector |
React component — goes in your UI bundle | |
@react-profiler-mcp/mcp-server |
Local server — HTTP ingest + MCP stdio |
Contributing
See CONTRIBUTING.md. Issues and PRs welcome.
To run locally:
git clone https://github.com/YOUR_USERNAME/react-profiler-mcp.git
cd react-profiler-mcp
npm install
npm run build
node packages/mcp-server/dist/index.js
Then in another terminal:
npm run dev -w @react-profiler-mcp/demo
Full contributor guide: docs/LOCAL_DEVELOPMENT.md
License
MIT — see LICENSE.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。