Network MCP Server

A Model Context Protocol (MCP) server providing network diagnostic tools for AI agents. Designed to offload heavy network analysis to the server and return structured, actionable data optimized for LLM consumption.

<p align="center"> <img src="https://raw.githubusercontent.com/labeveryday/network-mcp/main/docs/architecture.png" alt="Network MCP Architecture" width="800"/> </p>

Features

• Connectivity Testing: ping, traceroute, DNS lookups, port checks, MTR
• Batch Operations: Test multiple hosts/ports concurrently
• Local Network Info: Get interfaces, routes, DNS config, ARP table, connections (cross-platform)
• Pcap Analysis: Analyze packet captures with scapy (no tshark required)
• Custom Filters: Execute scapy filter expressions for advanced queries (AST-validated)
• Security Controls: Configurable allowlist/blocklist for target validation (enforced on connectivity tools)
• PCAP Path Guardrails: Restrict which directories the server is allowed to read captures from
• Smart Summaries: Returns human-readable summaries plus structured data

Installation

pip install network-mcp

Or install from source:

git clone https://github.com/labeveryday/network-mcp.git
cd network-mcp
pip install -e .

Quick Start

Run the MCP server:

network-mcp

Or with Python:

python -m network_mcp.server

Recommended first run (helps agents decide what’s safe/available on this host):

• Check capabilities: call capabilities to see installed binaries (like mtr), active security policy, and PCAP path guardrails.
• Decide on policy: if you need to loosen/tighten target validation or PCAP access, set env vars before launching.
• Run unit tests by default: pytest skips integration tests unless you opt in (see Development).

Available Tools

Diagnostics

Planning (Pure CIDR/VLAN math)

Designed for Tier 1 / Tier 2 NOC workflows. These tools are pure (no network calls, deterministic outputs) and safe to use for planning and validation.

Input examples

• VLAN map (both formats accepted):

{
  "10": "192.168.10.0/24",
  "20": { "cidr": "192.168.20.0/24", "name": "Voice" }
}

• plan_subnets requirements (aliases supported: hosts and prefix):

[
  { "vlan_id": 10, "name": "Users", "hosts": 120 },
  { "vlan_id": 20, "name": "Voice", "hosts": 60 },
  { "vlan_id": 30, "name": "Printers", "prefix": 26 }
]

External Intel

Connectivity Tools

Batch Operations

Local Network Info Tools

Cross-platform tools that work on Linux, macOS, and Windows.

Pcap Analysis Tools

IDE Integration

Claude Desktop

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

{
  "mcpServers": {
    "network-tools": {
      "command": ["network-mcp"]
    }
  }
}

Cursor

Add to your MCP settings:

{
  "mcpServers": {
    "network-tools": {
      "command": ["network-mcp"]
    }
  }
}

Using with uv

If you installed with uv:

{
  "mcpServers": {
    "network-tools": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/network-mcp", "network-mcp"]
    }
  }
}

Example Responses

Ping

{
  "success": true,
  "target": "google.com",
  "resolved_ip": "142.250.80.46",
  "packets_sent": 4,
  "packets_received": 4,
  "packet_loss_percent": 0.0,
  "min_latency_ms": 11.2,
  "avg_latency_ms": 12.8,
  "max_latency_ms": 15.1,
  "summary": "google.com is reachable. 4/4 packets received, avg latency 12.8ms"
}

Batch Ping

{
  "success": true,
  "total_targets": 3,
  "successful": 3,
  "failed": 0,
  "results": [
    {"target": "8.8.8.8", "success": true, "avg_latency_ms": 12.5},
    {"target": "1.1.1.1", "success": true, "avg_latency_ms": 8.2},
    {"target": "google.com", "success": true, "avg_latency_ms": 15.1}
  ],
  "summary": "Batch ping: 3/3 targets reachable"
}

TCP Issues Detection

{
  "success": true,
  "file_path": "/tmp/capture.pcap",
  "total_tcp_packets": 15234,
  "issues": [
    {
      "issue_type": "retransmission",
      "count": 47,
      "severity": "medium",
      "recommendation": "Retransmissions indicate packet loss. Check for network congestion."
    }
  ],
  "has_issues": true,
  "summary": "TCP issues detected in 15234 packets: 47 retransmissions"
}

Throughput (from PCAP)

{
  "success": true,
  "file_path": "/tmp/capture.pcap",
  "total_packets_scanned": 100000,
  "conversations_analyzed": 87,
  "top_n": 3,
  "sort_by": "mbps_total",
  "conversations": [
    {
      "src_ip": "10.0.0.10",
      "src_port": 51544,
      "dst_ip": "93.184.216.34",
      "dst_port": 443,
      "protocol": "TCP",
      "packets_total": 1240,
      "bytes_total": 18423333,
      "duration_seconds": 9.84,
      "start_time": 1734567890.01,
      "end_time": 1734567899.85,
      "packets_forward": 820,
      "bytes_forward": 17600000,
      "packets_reverse": 420,
      "bytes_reverse": 823333,
      "mbps_forward": 14.308,
      "mbps_reverse": 0.669,
      "mbps_total": 14.977,
      "direction": "10.0.0.10:51544 -> 93.184.216.34:443"
    }
  ],
  "summary": "Throughput analysis: 87 conversations from 100000 packets. Top flow 10.0.0.10:51544 -> 93.184.216.34:443 at ~14.977 Mbps over 9.84s"
}

Get Interfaces

{
  "success": true,
  "interfaces": [
    {
      "name": "en0",
      "status": "up",
      "mac_address": "00:11:22:33:44:55",
      "ipv4_addresses": ["192.168.1.100"],
      "ipv6_addresses": ["fe80::1"],
      "netmask": "255.255.255.0",
      "mtu": 1500
    }
  ],
  "default_interface": "en0",
  "summary": "Found 5 interfaces (3 up). Primary: en0"
}

Get Public IP

{
  "success": true,
  "public_ip": "203.0.113.42",
  "service_used": "ipify.org",
  "summary": "Public IP: 203.0.113.42 (via ipify.org)"
}

RDAP Lookup (WHOIS-style)

{
  "success": true,
  "query": "1.1.1.1",
  "query_type": "ip",
  "rdap_url": "https://rdap.org/ip/1.1.1.1",
  "handle": "NET-1-1-1-0-1",
  "country": "AU",
  "start_address": "1.1.1.0",
  "end_address": "1.1.1.255",
  "summary": "RDAP 1.1.1.1: 1.1.1.0–1.1.1.255 (AU), handle NET-1-1-1-0-1"
}

ASN Lookup

{
  "success": true,
  "ip": "1.1.1.1",
  "asn": "13335",
  "prefix": "1.1.1.0/24",
  "country": "AU",
  "registry": "apnic",
  "allocated": "2011-08-11",
  "as_name": "CLOUDFLARENET",
  "summary": "1.1.1.1 originates from AS13335 (CLOUDFLARENET), prefix 1.1.1.0/24"
}

Configuration

Create config.yaml in your working directory or ~/.network-mcp/config.yaml:

security:
  # Only allow these targets (glob patterns, CIDR ranges)
  allowed_targets:
    - "*.company.com"
    - "10.0.0.0/8"
    - "192.168.0.0/16"

  # Block these targets
  blocked_targets:
    - "*.gov"
    - "localhost"
    - "127.0.0.0/8"

  # Block private IPs
  block_private: false

  # Block cloud metadata endpoints (AWS, GCP, etc.)
  block_cloud_metadata: true

pcap:
  max_packets: 100000
  allow_custom_filters: true
  # Restrict which directories the server is allowed to read pcaps from.
  # (Paths are resolved before checking.)
  allowed_paths:
    - "."
    - "~/Documents"
    - "/tmp"

Environment variables:

NETWORK_MCP_ALLOWED_TARGETS="*.company.com,10.0.0.0/8"
NETWORK_MCP_BLOCKED_TARGETS="*.gov,localhost"
NETWORK_MCP_BLOCK_PRIVATE="true"
NETWORK_MCP_MAX_PACKETS="50000"
NETWORK_MCP_PCAP_ALLOWED_PATHS=".,~/Documents,/tmp"

Why MCP for Network Tools?

Token Efficiency: LLMs have context limits. The server does heavy processing (parsing 100k packets) and returns concise summaries instead of raw data.

Better Reasoning: LLMs excel at deciding what to investigate, not parsing raw output. Structured data leads to better decisions.

Consistency: Server-side processing is deterministic. You don't rely on the LLM to correctly interpret traceroute output every time.

Examples

The examples/ directory contains working examples using Strands Agents:

Quick start:

cd examples
pip install strands-agents strands-agents-tools 'strands-agents[ollama]'
python ollama_agent.py

See examples/README.md for full documentation.

Development

# Clone and install dev dependencies
git clone https://github.com/labeveryday/network-mcp.git
cd network-mcp
pip install -e ".[dev]"

# Run unit tests (integration tests are skipped by default)
pytest

# Run integration tests (requires system tools and/or network access)
pytest -m integration

# Run linting
ruff check .

Project Structure

network-mcp/
├── src/network_mcp/
│   ├── __init__.py
│   ├── server.py           # FastMCP server
│   ├── config.py           # Configuration and security
│   ├── tools/
│   │   ├── connectivity.py # ping, traceroute, dns, port_check, mtr, batch ops
│   │   ├── local.py        # local network info (interfaces, routes, etc.)
│   │   └── pcap.py         # pcap analysis tools
│   └── models/
│       └── responses.py    # Pydantic response models
├── tests/
├── pyproject.toml
└── README.md

Requirements

• Python 3.10+
• System tools: ping, traceroute (standard on most systems)
• Optional: mtr for the MTR tool

License

MIT License - see LICENSE file for details.