io.github.Lohithry/divlens — DivLens MCP Server

DivLens MCP provides real-time system diagnostics for AI agents. It exposes 17 tools covering CPU, RAM, disk, network, processes, and hardware health. The server is intended to give tools “eyes into your machine” by surfacing ongoing machine state data.

🛠️ Key Features

Real-time system diagnostics
CPU, RAM, disk, and network metrics
Process and hardware health visibility
17 tools in total
Server identity: io.github.Lohithry/divlens

🚀 Use Cases

Supplying AI agents with live system context
Monitoring machine health while running agent workflows
Inspecting processes alongside resource utilization

⚡ Developer Benefits

Structured tool access to system information (17 tools)
Applicable to agents needing CPU/RAM/disk/network awareness
Designed for integration with AI tools such as Claude, Cursor, and Windsurf (as referenced)

⚠️ Limitations

Capabilities are described only at the diagnostics and visibility level (no additional guarantees stated in the provided excerpt).

DivLens Logo

DivLens MCP

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

MCP Compatible Platform Version

Claude Cursor Windsurf Zero Cloud

What is DivLens MCP?

DivLens MCP is a high-performance Model Context Protocol (MCP) server written in Rust.

It 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.

No cloud. No API keys. No configuration required. Just build and run.

code

"Why is my Mac slow?" → Claude calls get_live_metrics() → Instant answer.
"Is my SSD healthy?"  → Claude calls get_hardware_diagnostics() → SMART data returned.
"What's eating disk?"  → Claude calls get_advanced_storage_stats() → Largest files listed.

✦ 17 Diagnostic Tools

Category	Tool	What it returns
⚡ Performance	`get_live_metrics`	CPU %, RAM, swap, blocked processes, uptime
⚡ Performance	`get_process_list`	Top processes by CPU / RAM with PID
💾 Storage	`get_storage_health`	Free/used/total per mount point
💾 Storage	`scan_storage_inventory`	Full file-type inventory with sizes
💾 Storage	`get_file_type_summary`	File counts and sizes by extension
💾 Storage	`get_specific_file_type`	All files matching a specific extension
💾 Storage	`get_advanced_storage_stats`	Top 50 largest files + stale data analysis
💾 Storage	`get_storage_diagnostics`	IOPS, read/write latency, SMART status
🖥️ Hardware	`get_hardware_diagnostics`	CPU/GPU specs, battery %, temps, SMART
🌐 Network	`get_network_diagnostics`	Throughput, active connections, signal
🌐 Network	`get_network_config`	IP, DNS, interface config per adapter
🔬 Identity	`get_system_dna`	OS, hostname, uptime, machine fingerprint
🛠️ Dev Stack	`get_dev_stack`	Node, Python, Rust, Go, Java runtimes + packages
🛠️ Dev Stack	`get_drivers`	Kernel modules and device drivers
📂 Utility	`scan_directory`	Recursive directory listing with sizes
🧠 Memory	`recall_memory`	Semantic search over past AI diagnoses
📋 Logs	`get_system_logs`	Recent OS/kernel errors clustered by pattern

🚀 Install — One Command, Any Platform

No Rust required. No compilation. No manual config editing. The installer downloads a pre-built binary and automatically configures your AI clients.

macOS & Linux

bash

curl -fsSL https://raw.githubusercontent.com/Lohithry/divlens-mcp/main/install.sh | bash

Windows (PowerShell — no admin required)

powershell

irm https://raw.githubusercontent.com/Lohithry/divlens-mcp/main/install.ps1 | iex

The installer will:

✅ Detect your OS and chip (Apple Silicon / Intel / Linux / Windows)
✅ Download the correct pre-built binary from GitHub Releases
✅ Verify the SHA-256 checksum
✅ Install to your PATH with no admin rights needed
✅ Auto-configure Claude Desktop, Cursor, Windsurf, and Antigravity
✅ Test the server works before finishing

Then just restart your AI client and ask "What's using my CPU right now?"

Build from Source (Advanced)

Requires Rust 1.82+.

bash

git clone https://github.com/Lohithry/divlens-mcp.git
cd divlens-mcp/apps/core
cargo build --release
./target/release/divlens-core --mcp

Connect to Your AI

Claude Desktop

Config file: ~/Library/Application Support/Claude/claude_desktop_config.json (macOS)
or %APPDATA%\Claude\claude_desktop_config.json (Windows)

jsonc

{
  "mcpServers": {
    "divlens": {
      "command": "/usr/local/bin/divlens-core",
      "args": ["--mcp"]
    }
  }
}

Quit and relaunch Claude Desktop. A 🔌 plug icon confirms the connection.

Cursor

Config file: ~/.cursor/mcp.json

jsonc

{
  "mcpServers": {
    "divlens": {
      "command": "/usr/local/bin/divlens-core",
      "args": ["--mcp"]
    }
  }
}

Cmd+Shift+P → Reload Window

Windsurf

Config file: ~/.codeium/windsurf/mcp_config.json

jsonc

{
  "mcpServers": {
    "divlens": {
      "command": "/usr/local/bin/divlens-core",
      "args": ["--mcp"]
    }
  }
}

For complete setup details, see DEPLOYMENT.md.

How It Works

code

  ┌─────────────────────────────────────────┐
  │   AI Client  (Claude / Cursor / etc.)   │
  │         LLM reasoning lives here        │
  └──────────────────┬──────────────────────┘
                     │  JSON-RPC 2.0  (stdio)
                     ▼
  ┌─────────────────────────────────────────┐
  │          divlens-core  (Rust)           │
  │                                         │
  │  ┌───────────────┐  ┌───────────────┐   │
  │  │  MCP Layer    │  │  17 Tools     │   │
  │  │  (JSON-RPC)   │  │  (Rust + OS)  │   │
  │  └───────────────┘  └───────────────┘   │
  │  ┌───────────────┐  ┌───────────────┐   │
  │  │  SQLite Cache │  │  Native APIs  │   │
  │  │  (sysinfo/OS) │  │  (IOKit/WMI)  │   │
  │  └───────────────┘  └───────────────┘   │
  └─────────────────────────────────────────┘

      Zero cloud.  Zero API keys.  100% local.

Transport: Every MCP message is a newline-delimited JSON-RPC 2.0 object over stdio.
AI logic: DivLens never runs LLM inference — it only collects and returns raw system data.
Privacy: All data stays on your machine. Nothing is sent anywhere.

Project Structure

code

divlens-mcp/
└── apps/
    └── core/                      # Rust MCP engine
        ├── src/
        │   ├── tools/             # 17 tool implementations
        │   ├── mcp/               # JSON-RPC 2.0 protocol handler
        │   ├── mcp_server.rs      # stdio transport loop
        │   ├── collectors/        # Native OS data collectors
        │   │   ├── volatile/      # CPU, RAM, network (live)
        │   │   ├── persistent/    # Storage, hardware (cached)
        │   │   └── ondemand/      # Drivers, logs, packages
        │   ├── modules/           # Core business logic
        │   ├── db/                # SQLite caching layer
        │   ├── models/            # Shared data types
        │   └── utils/             # Shell env rehydration
        ├── Cargo.toml
        └── env.example

Optional: Semantic Memory

Enable the vector-memory feature to give recall_memory true semantic search using a local ONNX embedding model (no cloud, no API key):

bash

cargo build --release --features vector-memory

When enabled, DivLens creates a local LanceDB vector store and uses fastembed to embed and recall past diagnoses semantically.

When disabled (default), recall_memory returns an empty list — no functionality is broken.

Verify the Server

Test the MCP wire protocol without a client:

bash

# Initialize handshake
echo '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","clientInfo":{"name":"test","version":"0.1"}}}' \
  | divlens-core --mcp

# Call a tool directly
echo '{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"get_live_metrics","arguments":{}}}' \
  | divlens-core --mcp

License

Licensed under the Apache License, Version 2.0.
See LICENSE for the full text.

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

io.github.Lohithry/divlens — DivLens MCP Server

🛠️ Key Features

🚀 Use Cases

⚡ Developer Benefits

⚠️ Limitations

DivLens MCP

What is DivLens MCP?

✦ 17 Diagnostic Tools

🚀 Install — One Command, Any Platform

macOS & Linux

Windows (PowerShell — no admin required)

Build from Source (Advanced)

Connect to Your AI

Claude Desktop

Cursor

Windsurf

How It Works

Project Structure

Optional: Semantic Memory

Verify the Server

License

Install

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