perfonext-profiler-mcp

# perfonext-profiler-mcp

> Analyze V8 and Chrome CPU profiles to find hotspots in Next.js servers and scripts.

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`perfonext-profiler-mcp` is a Model Context Protocol (MCP) server that gives GitHub Copilot, Claude Desktop,
Claude Code, and other MCP clients structured CPU profiling data for Next.js performance work. It loads V8 and
Chrome CPU profiles and turns them into hotspot rankings, per-package costs, and source-annotated hot lines —
evidence agents can reason over instead of ingesting multi-megabyte profile dumps.

## Quick Start

`perfonext-profiler-mcp` is a standard MCP stdio server, so it works with any MCP-compatible client
(GitHub Copilot in VS Code, Claude Desktop, Claude Code, Cursor, and others). Run it directly with `npx`:

```bash
npx -y @perfonext/profiler-mcp
```

Or install globally:

```bash
npm install -g @perfonext/profiler-mcp
```

The executable command remains `perfonext-profiler-mcp` after installation.

### VS Code

Add the server to `.vscode/mcp.json` (the workspace MCP configuration file):

```json
{
  "servers": {
    "perfonext-profiler": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@perfonext/profiler-mcp"]
    }
  }
}
```

Reload the VS Code window and run **MCP: List Servers** to start it, or accept the trust prompt when it appears.

### Claude Desktop

Add the server to `claude_desktop_config.json`:

```json
{
  "mcpServers": {
    "perfonext-profiler": {
      "command": "npx",
      "args": ["-y", "@perfonext/profiler-mcp"]
    }
  }
}
```

Restart Claude Desktop to pick up the new server.

### Claude Code

Add the server with the CLI:

```bash
claude mcp add perfonext-profiler -- npx -y @perfonext/profiler-mcp
```

Or add the same `mcpServers` entry to `.mcp.json`.

### Other MCP clients

Any client that supports stdio MCP servers can launch `npx -y @perfonext/profiler-mcp`. Consult your
client's documentation for its MCP server configuration format.

For a locally-built checkout, point `command`/`args` at `node` and the repo's `dist/index.js` instead.

## Troubleshooting

### `spawn npx ENOENT` / `spawn node ENOENT` on macOS with nvm

If the server fails to start with this error, your GUI MCP client likely cannot see nvm. GUI apps on
macOS do not load shell config (`.zshrc`/`.bashrc`), so nvm-installed `npx`/`node` are not on `PATH`.
Use an absolute `npx` path and include the same Node directory in `PATH`:

```json
{
  "servers": {
    "perfonext-profiler": {
      "type": "stdio",
      "command": "/Users/YOU/.nvm/versions/node/v<version>/bin/npx",
      "args": ["-y", "@perfonext/profiler-mcp"],
      "env": {
        "PATH": "/Users/YOU/.nvm/versions/node/v<version>/bin:/usr/bin:/bin"
      }
    }
  }
}
```

Merge these fields into your client's server entry, under `servers` for VS Code or `mcpServers` for
Claude Desktop/Code. Then ask your assistant: _"How do I capture a CPU profile of my Next.js server?"_

## What It Does

- loads `.cpuprofile` files and Chrome trace exports that contain CPU profile data
- identifies the hottest functions by self time, annotated with the originating npm package
- explains caller and callee relationships for a selected function
- **reads actual source code for hot functions and annotates each line with V8 sample counts** (v0.2.0)
- **aggregates CPU self-time per npm package to find expensive third-party dependencies** (v0.3.0)
- compares two profiles to surface regressions and improvements
- returns deterministic optimization suggestions for common hotspots
- summarizes loaded profiles so an MCP client can keep context tight

## Tools

| Tool                    | Description                                                                                                                                                                                                        |
| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `how_to_collect`        | Return a ready-to-run command and step-by-step recipe for capturing a `.cpuprofile`, then loading it. Use this when you don't have a profile yet                                                                   |
| `load_profile`          | Parse and load a `.cpuprofile` file or Chrome trace export from disk                                                                                                                                               |
| `get_hotspots`          | Find top functions by self-time. Each entry includes a `package` field identifying the npm package or `(user code)`                                                                                                |
| `explain_function`      | Explain a function's timing, callers, and callees. Pass `includeSource: true` to attach annotated source lines                                                                                                     |
| `read_source_context`   | Read the actual source file for a hot function and annotate each line with tick counts from `positionTicks`                                                                                                        |
| `get_package_costs`     | Aggregate CPU self-time by npm package — shows which dependencies are most expensive                                                                                                                               |
| `compare_profiles`      | Compare two profiles and highlight regressions                                                                                                                                                                     |
| `suggest_optimizations` | Generate structured, multi-pattern optimization suggestions for hot functions. Detects high fan-in, recursion, dominant callers, and V8-specific patterns. Deduplicates functions split across multiple call sites |
| `get_profile_summary`   | Summarize one profile or list all loaded profiles                                                                                                                                                                  |

Every tool result carries a `nextStep` breadcrumb pointing at the natural follow-up call, so an MCP client can walk the collect → analyze → fix loop without guessing.

## Example Prompts

- "How do I capture a CPU profile of my Next.js server?"
- "Load the CPU profile at `./profile.cpuprofile` and show me the top hotspots."
- "Which npm packages are consuming the most CPU in this profile?"
- "Explain why `processData` is expensive in the loaded profile."
- "Show me the actual source lines for `processData` and mark which lines are hottest."
- "Explain `transformResult` and include the annotated source code."
- "Compare my baseline and current CPU profiles and tell me what got slower."
- "Suggest optimizations for the top three hotspots."

## Deep Tool Reference

<details>
<summary>Per-tool input/output schemas and manual profile capture</summary>

### `how_to_collect` details

```jsonc
// Input
{ "scenario": "next-server" } // or "script"; defaults to "next-server"

// Output
{
  "scenario": "next-server",
  "summary": "Profile a production Next.js server while it handles a single request. ...",
  "command": "node --cpu-prof --cpu-prof-dir=./.perf-profiles ./node_modules/next/dist/bin/next start",
  "steps": [ "...", "load_profile({ filePath: \"./.perf-profiles/<file>.cpuprofile\" })" ],
  "outputDir": "./.perf-profiles",
  "nextStep": "After stopping the server, call load_profile with the .cpuprofile ..."
}
```

`next-server` profiles a production Next.js server while it serves a single request. If `next start` says standalone output is unsupported, use the `script` scenario with `.next/standalone/server.js`. `script` profiles that standalone server (or another Node entry). Keep the scenario to one route and one hit. Node writes one `.cpuprofile` per process/worker thread into the output directory. The Next server command uses Node CLI flags (not `NODE_OPTIONS`) so it is the same on Unix and Windows.

### `read_source_context` details

```jsonc
// Input
{ "profileId": "<id>", "functionName": "myFn", "contextLines": 10 }

// Output (per line)
{
  "lineNumber": 42,
  "content": "  for (let i = 0; i < items.length; i++) {",
  "ticks": 18,      // V8 samples that landed on this line
  "isHot": true     // true when ticks >= 50% of peak ticks for this function
}
```

The returned window is sized to cover the function's actual hot lines, not just a fixed radius
around its declaration — a function's real bottleneck is often well past its `function` line.
`contextLines` (default 10) sets the minimum padding around both the declaration and the hot
lines; if any ticks still fall outside the returned window, the top-level result includes
`hiddenTicks` (a count) and a `warning` telling you to retry with a larger `contextLines`.
`explain_function` also accepts `contextLines` when called with `includeSource: true`.

Only files inside the current working directory can be read. `file://` URLs and absolute paths are both handled; `http://`, `node:` builtins, and paths outside the project root are rejected.

### `suggest_optimizations` details

```jsonc
// Input
{ "profileId": "<id>", "limit": 5 }

// Output (per function)
{
  "function": "processData",
  "file": "file:///app/src/processor.js",
  "line": 10,
  "selfPercent