Divlens

Real-time system diagnostics for AI agents — CPU, RAM, disk, network, hardware health. 17 tools.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

One-click editor setup isn’t available for this listing yet — we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.

Manual Client & Custom JSON ConfigExpand JSON ▾

No confirmed setup config for this listing yet. We only publish a config block when the install details come from the project itself — its README, its docs, or a verified owner. We haven’t found those for divlens, and we’d rather show nothing than a guess you’d paste into your client. Follow the project’s own setup instructions for the current steps.

Documentation Overview

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.

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

Terminal

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)

JSON Config

{
  "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

JSON Config

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

Cmd+Shift+P → Reload Window

Windsurf

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

JSON Config

{
  "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}

Read the full README →View source on GitHub →

Related MCP Servers

View all in Developer Tools View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Last commit

3mo ago

Most recent push to the default branch.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Divlens

We don't have a confirmed install command for divlens yet, so we don't publish a generated one — a guessed package name would point at the wrong package or none at all. Follow the project's own README or setup instructions (https://github.com/Lohithry/divlens-mcp) for the current steps.

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Technical Specs & Signals

CategoryDeveloper Tools

10/14 checks healthy over the last 45d

32Quality signal: Emerging · 32/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools11/30

Adoption & activity3/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

★ Featured

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More in 💻 Developer Tools →Best MCP servers for Developers →Alternatives to Divlens →Install in Claude Desktop Install in Cursor Install in VS Code Setup guides for all 13 MCP clients

Divlens

Real-time system diagnostics for AI agents — CPU, RAM, disk, network, hardware health. 17 tools.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Manual Client & Custom JSON ConfigExpand JSON ▾

Documentation Overview

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.

What is DivLens MCP?

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

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

Terminal

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)

JSON Config

{
  "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

JSON Config

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

Cmd+Shift+P → Reload Window

Windsurf

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

JSON Config

{
  "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.

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}

Read the full README →View source on GitHub →

Related MCP Servers

View all in Developer Tools View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Last commit

3mo ago

Most recent push to the default branch.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Divlens

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:

Live Dynamic SVG Preview

Markdown (GitHub README)

[![AllMCPs](https://allmcps.com/api/badge/divlens?style=directory)](https://allmcps.com/mcp/divlens)

HTML Embed

<a href="https://allmcps.com/mcp/divlens"><img src="https://allmcps.com/api/badge/divlens?style=directory" alt="Divlens on AllMCPs" /></a>

Technical Specs & Signals

CategoryDeveloper Tools

10/14 checks healthy over the last 45d

32Quality signal: Emerging · 32/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools11/30

Adoption & activity3/15

Community engagement0/10

★ Featured

Moxie Docs MCP

MCP & Agent Skills for Automated Documentation, and codebase conventions + context

Explore Server →

Own this project?

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

Share & Embed

Add our SVG badge (dark/light directory styles) or embeddable widget to your site.

Explore more

More in 💻 Developer Tools →Best MCP servers for Developers →Alternatives to Divlens →Install in Claude Desktop Install in Cursor Install in VS Code Setup guides for all 13 MCP clients