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\n $\"DivLens$ \n

\n\n

DivLens MCP

\n\n

\n Real-time system intelligence for AI agents.
\n Give Claude, Cursor, and Windsurf eyes into your machine — CPU, RAM, disk, network, processes, hardware health, and more.\n

\n\n

\n \n $\"License:$ \n \n \n $\"Built$ \n \n $\"MCP$ \n $\"Platform\"$ \n $\"Version\"$ \n

\n\n

\n $\"Claude\"$ \n $\"Cursor\"$ \n $\"Windsurf\"$ \n $\"Zero$ \n

\n\n---\n\n## What is DivLens MCP?\n\n**DivLens MCP** is a high-performance [Model Context Protocol (MCP)](https://modelcontextprotocol.io) server written in Rust.\n\nIt bridges the gap between AI assistants and your machine — giving Claude, Cursor, Windsurf, and any other MCP-compatible agent **live, structured access** to hardware sensors, storage metrics, network diagnostics, process trees, developer runtimes, system logs, and more.\n\nNo cloud. No API keys. No configuration required. Just build and run.\n\n```\n\"Why is my Mac slow?\" → Claude calls get_live_metrics() → Instant answer.\n\"Is my SSD healthy?\" → Claude calls get_hardware_diagnostics() → SMART data returned.\n\"What's eating disk?\" → Claude calls get_advanced_storage_stats() → Largest files listed.\n```\n\n---\n\n## ✦ 17 Diagnostic Tools\n\n| Category | Tool | What it returns |\n| :--- | :--- | :--- |\n| ⚡ **Performance** | `get_live_metrics` | CPU %, RAM, swap, blocked processes, uptime |\n| ⚡ **Performance** | `get_process_list` | Top processes by CPU / RAM with PID |\n| 💾 **Storage** | `get_storage_health` | Free/used/total per mount point |\n| 💾 **Storage** | `scan_storage_inventory` | Full file-type inventory with sizes |\n| 💾 **Storage** | `get_file_type_summary` | File counts and sizes by extension |\n| 💾 **Storage** | `get_specific_file_type` | All files matching a specific extension |\n| 💾 **Storage** | `get_advanced_storage_stats` | Top 50 largest files + stale data analysis |\n| 💾 **Storage** | `get_storage_diagnostics` | IOPS, read/write latency, SMART status |\n| 🖥️ **Hardware** | `get_hardware_diagnostics` | CPU/GPU specs, battery %, temps, SMART |\n| 🌐 **Network** | `get_network_diagnostics` | Throughput, active connections, signal |\n| 🌐 **Network** | `get_network_config` | IP, DNS, interface config per adapter |\n| 🔬 **Identity** | `get_system_dna` | OS, hostname, uptime, machine fingerprint |\n| 🛠️ **Dev Stack** | `get_dev_stack` | Node, Python, Rust, Go, Java runtimes + packages |\n| 🛠️ **Dev Stack** | `get_drivers` | Kernel modules and device drivers |\n| 📂 **Utility** | `scan_directory` | Recursive directory listing with sizes |\n| 🧠 **Memory** | `recall_memory` | Semantic search over past AI diagnoses |\n| 📋 **Logs** | `get_system_logs` | Recent OS/kernel errors clustered by pattern |\n\n---\n\n## 🚀 Install — One Command, Any Platform\n\n> **No Rust required. No compilation. No manual config editing.**\n> The installer downloads a pre-built binary and automatically configures your AI clients.\n\n### macOS & Linux\n\n```bash\ncurl -fsSL https://raw.githubusercontent.com/Lohithry/divlens-mcp/main/install.sh | bash\n```\n\n### Windows (PowerShell — no admin required)\n\n```powershell\nirm https://raw.githubusercontent.com/Lohithry/divlens-mcp/main/install.ps1 | iex\n```\n\nThe installer will:\n- ✅ Detect your OS and chip (Apple Silicon / Intel / Linux / Windows)\n- ✅ Download the correct pre-built binary from GitHub Releases\n- ✅ Verify the SHA-256 checksum\n- ✅ Install to your PATH with no admin rights needed\n- ✅ Auto-configure **Claude Desktop**, **Cursor**, **Windsurf**, and **Antigravity**\n- ✅ Test the server works before finishing\n\nThen just **restart your AI client** and ask *\"What's using my CPU right now?\"*\n\n---\n\n### Build from Source (Advanced)\n\nRequires [Rust](https://rustup.rs/) 1.82+.\n\n```bash\ngit clone https://github.com/Lohithry/divlens-mcp.git\ncd divlens-mcp/apps/core\ncargo build --release\n./target/release/divlens-core --mcp\n```\n\n---\n\n## Connect to Your AI\n\n### Claude Desktop\n\n> Config file: `~/Library/Application Support/Claude/claude_desktop_config.json` (macOS) \n> or `%APPDATA%\\Claude\\claude_desktop_config.json` (Windows)\n\n```jsonc\n{\n \"mcpServers\": {\n \"divlens\": {\n \"command\": \"/usr/local/bin/divlens-core\",\n \"args\": [\"--mcp\"]\n }\n }\n}\n```\n\nQuit and relaunch Claude Desktop. A 🔌 plug icon confirms the connection.\n\n### Cursor\n\n> Config file: `~/.cursor/mcp.json`\n\n```jsonc\n{\n \"mcpServers\": {\n \"divlens\": {\n \"command\": \"/usr/local/bin/divlens-core\",\n \"args\": [\"--mcp\"]\n }\n }\n}\n```\n\n`Cmd+Shift+P` → *Reload Window*\n\n### Windsurf\n\n> Config file: `~/.codeium/windsurf/mcp_config.json`\n\n```jsonc\n{\n \"mcpServers\": {\n \"divlens\": {\n \"command\": \"/usr/local/bin/divlens-core\",\n \"args\": [\"--mcp\"]\n }\n }\n}\n```\n\nFor complete setup details, see **[DEPLOYMENT.md](https://github.com/Lohithry/divlens-mcp/blob/main/DEPLOYMENT.md)**.\n\n---\n\n## How It Works\n\n```\n ┌─────────────────────────────────────────┐\n │ AI Client (Claude / Cursor / etc.) │\n │ LLM reasoning lives here │\n └──────────────────┬──────────────────────┘\n │ JSON-RPC 2.0 (stdio)\n ▼\n ┌─────────────────────────────────────────┐\n │ divlens-core (Rust) │\n │ │\n │ ┌───────────────┐ ┌───────────────┐ │\n │ │ MCP Layer │ │ 17 Tools │ │\n │ │ (JSON-RPC) │ │ (Rust + OS) │ │\n │ └───────────────┘ └───────────────┘ │\n │ ┌───────────────┐ ┌───────────────┐ │\n │ │ SQLite Cache │ │ Native APIs │ │\n │ │ (sysinfo/OS) │ │ (IOKit/WMI) │ │\n │ └───────────────┘ └───────────────┘ │\n └─────────────────────────────────────────┘\n\n Zero cloud. Zero API keys. 100% local.\n```\n\n**Transport:** Every MCP message is a newline-delimited JSON-RPC 2.0 object over stdio. \n**AI logic:** DivLens never runs LLM inference — it only collects and returns raw system data. \n**Privacy:** All data stays on your machine. Nothing is sent anywhere.\n\n---\n\n## Project Structure\n\n```\ndivlens-mcp/\n└── apps/\n └── core/ # Rust MCP engine\n ├── src/\n │ ├── tools/ # 17 tool implementations\n │ ├── mcp/ # JSON-RPC 2.0 protocol handler\n │ ├── mcp_server.rs # stdio transport loop\n │ ├── collectors/ # Native OS data collectors\n │ │ ├── volatile/ # CPU, RAM, network (live)\n │ │ ├── persistent/ # Storage, hardware (cached)\n │ │ └── ondemand/ # Drivers, logs, packages\n │ ├── modules/ # Core business logic\n │ ├── db/ # SQLite caching layer\n │ ├── models/ # Shared data types\n │ └── utils/ # Shell env rehydration\n ├── Cargo.toml\n └── env.example\n```\n\n---\n\n## Optional: Semantic Memory\n\nEnable the `vector-memory` feature to give `recall_memory` true semantic search using a local ONNX embedding model (no cloud, no API key):\n\n```bash\ncargo build --release --features vector-memory\n```\n\nWhen enabled, DivLens creates a local [LanceDB](https://lancedb.github.io/lancedb/) vector store and uses [fastembed](https://github.com/Anush008/fastembed-rs) to embed and recall past diagnoses semantically.\n\nWhen disabled (default), `recall_memory` returns an empty list — no functionality is broken.\n\n---\n\n## Verify the Server\n\nTest the MCP wire protocol without a client:\n\n```bash\n# Initialize handshake\necho '{\"jsonrpc\":\"2.0\",\"id\":1,\"method\":\"initialize\",\"params\":{\"protocolVersion\":\"2024-11-05\",\"clientInfo\":{\"name\":\"test\",\"version\":\"0.1\"}}}' \\\n | divlens-core --mcp\n\n# Call a tool directly\necho '{\"jsonrpc\":\"2.0\",\"id\":2,\"method\":\"tools/call\",\"params\":{\"name\":\"get_live_metrics\",\"arguments\":{}}}' \\\n | divlens-core --mcp\n```\n\n---\n\n## License\n\nLicensed under the **Apache License, Version 2.0**. \nSee [LICENSE](https://github.com/Lohithry/divlens-mcp/blob/main/LICENSE) for the full text.\n\nCopyright © 2024 DivLens Contributors.\n\n---\n\n

\n $\"DivLens\"$
\n _{Built with ❤️ in Rust · Zero cloud · AI-native diagnostics}\n

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