How do I install Pprof Analyzer?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Pprof Analyzer need an API key?

No required environment variable is listed on this record.

What transport does Pprof Analyzer use?

Pprof Analyzer runs locally over stdio.

Servidor MCP Pprof Analyzer

Q: When was Pprof Analyzer last checked?

Last checked 2026-10-28. This is a Model Context Protocol (MCP) server implemented in Go, providing a tool to analyze Go pprof performance profiles.

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Install from the publisher. Not a PlugMCP endpoint.

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Host a copy with our keys and logs.

No packaged release in the registry yet. Follow the repository README.

RepositoryPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

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Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

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Server facts

Local

Transport: stdio
License: MIT
Last checked: 2026-10-28

Tools

No tool list yet. Remote servers get one after the first health check.

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简体中文 | English

Pprof Analyzer MCP Server

This is a Model Context Protocol (MCP) server implemented in Go, providing a tool to analyze Go pprof performance profiles. Built with the official Model Context Protocol Go SDK.

Features

analyze_pprof Tool:
- Analyzes the specified Go pprof file and returns serialized analysis results (e.g., Top N list or flame graph JSON).
- Supported Profile Types:
  - cpu: Analyzes CPU time consumption during code execution to find hot spots.
  - heap: Analyzes the current memory usage (heap allocations) to find objects and functions with high memory consumption. Enhanced with object count, allocation site, and type information.
  - goroutine: Displays stack traces of all current goroutines, used for diagnosing deadlocks, leaks, or excessive goroutine usage.
  - allocs: Analyzes memory allocations (including freed ones) during program execution to locate code with frequent allocations. Provides detailed allocation site and object count information.
  - mutex: Analyzes contention on mutexes to find locks causing blocking. Provides detailed statistics including contention counts, delay times, and percentages.
  - block: Analyzes operations causing goroutine blocking (e.g., channel waits, system calls). Provides comprehensive blocking statistics with average delay calculations.
- Supported Output Formats: text, markdown, json (Top N list), flamegraph-json (hierarchical flame graph data, default).
  - text, markdown: Human-readable text or Markdown format.
  - json: Outputs Top N results in structured JSON format (implemented for cpu, heap, goroutine, allocs, mutex, block).
  - flamegraph-json: Outputs hierarchical flame graph data in JSON format, compatible with d3-flame-graph (implemented for cpu, heap, allocs, default format). Output is compact.
- Configurable number of Top N results (top_n, defaults to 5, effective for text, markdown, json formats).
generate_flamegraph Tool:
- Uses go tool pprof to generate a flame graph (SVG format) for the specified pprof file, saves it to the specified path, and returns the path and SVG content.
- Supported Profile Types: cpu, heap, allocs, goroutine, , .

Installation (As a Library/Tool)

You can install this package directly using go install:

go install github.com/ZephyrDeng/pprof-analyzer-mcp@latest

This will install the pprof-analyzer-mcp executable to your $GOPATH/bin or $HOME/go/bin directory. Ensure this directory is in your system's PATH to run the command directly.

Building from Source

Ensure you have a Go environment installed (Go 1.18 or higher recommended).

In the project root directory (pprof-analyzer-mcp), run:

go build

This will generate an executable file named pprof-analyzer-mcp (or pprof-analyzer-mcp.exe on Windows) in the current directory.

Using `go install` (Recommended)

You can also use go install to install the executable into your $GOPATH/bin or $HOME/go/bin directory. This allows you to run pprof-analyzer-mcp directly from the command line (if the directory is added to your system's PATH environment variable).

# Installs the executable using the module path defined in go.mod
go install .
# Or directly using the GitHub path (recommended after publishing)
# go install github.com/ZephyrDeng/pprof-analyzer-mcp@latest

Running with Docker

Using Docker is a convenient way to run the server, as it bundles the necessary Graphviz dependency.

Build the Docker Image: In the project root directory (where the Dockerfile is located), run:
```
docker build -t pprof-analyzer-mcp .
```
Run the Docker Container:
```
docker run -i --rm pprof-analyzer-mcp
```
- The -i flag keeps STDIN open, which is required for the stdio transport used by this MCP server.
- The --rm flag automatically removes the container when it exits.
Configure MCP Client for Docker: To connect your MCP client (like Roo Cline) to the server running inside Docker, update your .roo/mcp.json:
```
{
  "mcpServers": {
    "pprof-analyzer-docker": {
      "command": "docker run -i --rm pprof-analyzer-mcp"
    }
  }
}
```
Make sure the pprof-analyzer-mcp image has been built locally before the client tries to run this command.

Releasing (Automated via GitHub Actions)

This project uses GoReleaser and GitHub Actions to automate the release process. Releases are triggered automatically when a Git tag matching the pattern v* (e.g., v0.1.0, v1.2.3) is pushed to the repository.

Pre-release Checklist:

Before creating a release tag, ensure:

✅ All tests pass: go test ./...
✅ Code compiles successfully: go build
✅ Documentation is up to date (README, CHANGELOG, etc.)
✅ Commit messages follow Conventional Commits format

Release Steps:

Make Changes: Develop new features or fix bugs.

Commit Changes: Commit your changes using Conventional Commits format (e.g., feat: ..., fix: ..., docs: ...). This is important for automatic changelog generation.

git add .
git commit -m "feat: Add awesome new feature"
# or
git commit -m "fix: Resolve issue #42"
# or
git commit -m "docs: Update README for new feature"

Push Changes: Push your commits to the main branch on GitHub.
```
git push origin main
```
Run Pre-release Tests: Optionally, run tests locally before tagging:
```
go test ./... -v
go build -v
```

Create and Push Tag: When ready to release, create a new Git tag and push it to GitHub.

# Example: Create tag v0.2.0
git tag v0.2.0

# Push the tag to GitHub
git push origin v0.2.0

Automatic Release: Pushing the tag will trigger the GoReleaser GitHub Action defined in .github/workflows/release.yml. This action will:
- Build binaries for Linux, macOS, and Windows (amd64 & arm64).
- Generate a changelog based on Conventional Commits since the last tag.
- Create a new GitHub Release with the changelog and attach the built binaries and checksums as assets.

Monitoring the Release:

You can view the release workflow progress in the "Actions" tab of the GitHub repository. Once complete, the release will be available at:

https://github.com/ZephyrDeng/pprof-analyzer-mcp/releases

Configuring the MCP Client

This server uses the stdio transport protocol. You need to configure it in your MCP client (e.g., Roo Cline extension for VS Code).

Typically, this involves adding the following configuration to the .roo/mcp.json file in your project root:

{
  "mcpServers": {
    "pprof-analyzer": {
      "command": "pprof-analyzer-mcp"
    }
  }
}

Note: Adjust the command value based on your build method (go build or go install) and the actual location of the executable. Ensure the MCP client can find and execute this command.

After configuration, reload or restart your MCP client, and it should automatically connect to the PprofAnalyzer server.

Dependencies

Graphviz: The generate_flamegraph tool requires Graphviz to generate SVG flame graphs (the go tool pprof command calls dot when generating SVG). Ensure Graphviz is installed on your system and the dot command is available in your system's PATH environment variable.

Installing Graphviz:
- macOS (using Homebrew):
```
brew install graphviz
```
- Debian/Ubuntu:
```
sudo apt-get update && sudo apt-get install graphviz
```
- CentOS/Fedora:
```
sudo yum install graphviz
# or
sudo dnf install graphviz
```
- Windows (using Chocolatey):
```
choco install graphviz
```
- Other Systems: Refer to the Graphviz official download page.

Usage Examples (via MCP Client)

Once the server is connected, you can call the analyze_pprof and generate_flamegraph tools using file://, http://, or https:// URIs for the profile file.

Example: Analyze CPU Profile (Text format, Top 5)

{
  "tool_name": "analyze_pprof",
  "arguments": {
    "profile_uri": "file:///path/to/your/cpu.pprof",
    "profile_type": "cpu"
  }
}

Example: Analyze Heap Profile (Markdown format, Top 10)

{
  "tool_name": "analyze_pprof",
  "arguments": {
    "profile_uri": "file:///path/to/your/heap.pprof",
    "profile_type": "heap",
    "top_n": 10,
    "output_format": "markdown"
  }
}

Example: Analyze Goroutine Profile (Text format, Top 5)

{
  "tool_name": "analyze_pprof",
  "arguments": {
    "profile_uri": "file:///path/to/your/goroutine.pprof",
    "profile_type": "goroutine"
  }
}

Example: Generate Flame Graph for CPU Profile

{
  "tool_name": "generate_flamegraph",
  "arguments": {
    "profile_uri": "file:///path/to/your/cpu.pprof",
    "profile_type": "cpu",
    "output_svg_path": "/path/to/save/cpu_flamegraph.svg"
  }
}

Example: Generate Flame Graph for Heap Profile (inuse_space)

{
  "tool_name": "generate_flamegraph",
  "arguments": {
    "profile_uri": "file:///path/to/your/heap.pprof",
    "profile_type": "heap",
    "output_svg_path": "/path/to/save/heap_flamegraph.svg"
  }
}

Example: Analyze CPU Profile (JSON format, Top 3)

{
  "tool_name": "analyze_pprof",
  "arguments": {
    "profile_uri": "file:///path/to/your/cpu.pprof",
    "profile_type": "cpu",
    "top_n": 3,
    "output_format": "json"
  }
}

Example: Analyze CPU Profile (Default Flame Graph JSON format)

{
  "tool_name": "analyze_pprof",
  "arguments": {
    "profile_uri": "file:///path/to/your/cpu.pprof",
    "profile_type": "cpu"
    // output_format defaults to "flamegraph-json"
  }
}

Example: Analyze Heap Profile (Explicitly Flame Graph JSON format)

{
  "tool_name": "analyze_pprof",
  "arguments": {
    "profile_uri": "file:///path/to/your/heap.pprof",
    "profile_type": "heap",
    "output_format": "flamegraph-json"
  }
}

Example: Analyze Remote CPU Profile (from HTTP URL)

{
  "tool_name": "analyze_pprof",
  "arguments": {
    "profile_uri": "https://example.com/profiles/cpu.pprof",
    "profile_type": "cpu"
  }
}

Example: Analyze Online CPU Profile (from GitHub Raw URL)

{
  "tool_name": "analyze_pprof",
  "arguments": {
    "profile_uri": "https://raw.githubusercontent.com/google/pprof/refs/heads/main/profile/testdata/gobench.cpu",
    "profile_type": "cpu",
    "top_n": 5
  }
}

Example: Generate Flame Graph for Online Heap Profile (from GitHub Raw URL)

{
  "tool_name": "generate_flamegraph",
  "arguments": {
    "profile_uri": "https://raw.githubusercontent.com/google/pprof/refs/heads/main/profile/testdata/gobench.heap",
    "profile_type": "heap",
    "output_svg_path": "./online_heap_flamegraph.svg"
  }
}

Example: Open Interactive Pprof UI for Online CPU Profile (macOS Only)

{
  "tool_name": "open_interactive_pprof",
  "arguments": {
    "profile_uri": "https://raw.githubusercontent.com/google/pprof/refs/heads/main/profile/testdata/gobench.cpu"
    // Optional: "http_address": ":8082" // Example of overriding the default port
  }
}

Example: Detect Memory Leaks Between Two Heap Profiles

{
  "tool_name": "detect_memory_leaks",
  "arguments": {
    "old_profile_uri": "file:///path/to/your/heap_before.pprof",
    "new_profile_uri": "file:///path/to/your/heap_after.pprof",
    "threshold": 0.05,  // 5% growth threshold
    "limit": 15         // Show top 15 potential leaks
  }
}

Example: Disconnect a Pprof Session

{
  "tool_name": "disconnect_pprof_session",
  "arguments": {
    "pid": 12345 // Replace 12345 with the actual PID returned by open_interactive_pprof
  }
}

Future Improvements (TODO)

Add MIME type handling in MCP results based on output_format.
Add more robust error handling and logging level control.
Add integration tests for end-to-end MCP tool interactions.
Performance optimizations for large profile files (>1GB).

Recently Completed (v0.3.0)

✅ ~~Implement differential flame graphs to visualize changes between profiles.~~ (Done - compare_profiles tool)
✅ ~~Add time-series analysis for memory profiles to track growth over multiple snapshots.~~ (Done - analyze_heap_time_series tool)
✅ Add automated CI/CD with GitHub Actions testing on every PR.
✅ ~~Implement full analysis logic for mutex, block profiles.~~ (Done in v0.2.0)
✅ ~~Implement json output format for mutex, block profile types.~~ (Done in v0.2.0)
✅ Migrated to official Model Context Protocol Go SDK.
✅ ~~Consider supporting remote pprof file URIs (e.g., http://, https://).~~ (Done in v0.2.0)
✅ ~~Implement full analysis logic for allocs profiles.~~ (Done in v0.2.0)
✅ ~~Implement json output format for allocs profile type.~~ (Done in v0.2.0)
✅ ~~Add memory leak detection capabilities.~~ (Done in v0.2.0)

Añadir este servidor

Host a copy with our keys and logs.

Server facts

Tools

Questions

How do I install Pprof Analyzer?

Does Pprof Analyzer need an API key?

What transport does Pprof Analyzer use?

When was Pprof Analyzer last checked?

Documentation

Pprof Analyzer MCP Server

Features

Installation (As a Library/Tool)

Building from Source

Using go install (Recommended)

Running with Docker

Releasing (Automated via GitHub Actions)

Configuring the MCP Client

Dependencies

Usage Examples (via MCP Client)

Future Improvements (TODO)

Recently Completed (v0.3.0)

Using `go install` (Recommended)