FastMCP server providing LLM-optimized access to Scalene profiler
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# Scalene-MCP A FastMCP v2 server providing LLMs with structured access to [Scalene](https://github.com/plasma-umass/scalene)'s comprehensive CPU, GPU, and memory profiling capabilities. ## Installation ### Prerequisites - Python 3.10+ - uv (recommended) or pip ### From Source ```bash git clone https://github.com/plasma-umass/scalene-mcp.git cd scalene-mcp uv venv uv sync ``` ### As a Package ```bash pip install scalene-mcp ``` ## Quick Start: Running the Server ### Development Mode ```bash # Using uv uv run scalene_mcp.server # Using pip python -m scalene_mcp.server ``` ### Production Mode ```bash python -m scalene_mcp.server ``` ## 🎯 Native Integration with VSCode LLM Editors **Works seamlessly with:** - ✅ **[GitHub Copilot](SETUP_GITHUB_COPILOT.md)** - Direct integration - ✅ **[Claude Code](SETUP_CLAUDE.md)** - VSCode extension - ✅ **[Cursor](SETUP_CURSOR.md)** - All-in-one IDE - ✅ **Any MCP-compatible LLM client** ### Zero-Friction Setup (3 Steps) 1. **Install** ```bash pip install scalene-mcp ``` 2. **Configure** - Choose one method: **Automated (Recommended):** ```bash python scripts/setup_vscode.py ``` Interactive setup script auto-finds your editor and configures it. **Manual - GitHub Copilot:** ```json // .vscode/settings.json { "github.copilot.chat.mcp.servers": { "scalene": { "command": "uv", "args": ["run", "-m", "scalene_mcp.server"] } } } ``` **Manual - Claude Code / Cursor:** See editor-specific setup guides 3. **Restart VSCode/Cursor** and start profiling! ### Start Profiling Immediately Open any Python project and ask your LLM: ``` "Profile main.py and show me the bottlenecks" ``` The LLM automatically: - 🔍 Detects your project structure - 📄 Finds and profiles your code - 📊 Analyzes CPU, memory, GPU usage - 💡 Suggests optimizations **No path thinking. No manual configuration. Zero friction.** 📚 **Editor-Specific Setup:*…
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