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

Tool	Description
capabilities	Report runtime capabilities (installed binaries like mtr, active security policy, pcap path guardrails) so agents can plan tool usage

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.

Tool	Description
cidr_info	CIDR primitives (IPv4/IPv6): network, usable range, mask/wildcard, counts
ip_in_subnet	Check whether an IP is in a subnet and whether it’s a usable host address
subnet_split	Split a CIDR into equal-size child subnets (by new prefix or power-of-two count)
cidr_summarize	Collapse/aggregate CIDRs into summarized routes (IPv4/IPv6 handled separately)
check_overlaps	Find overlaps/containment conflicts between CIDRs
validate_vlan_map	Validate a 1-subnet-per-VLAN map and surface overlaps
find_vlan_for_ip	Find which VLAN matches an IP from a provided VLAN map (Tier 1 “where does this belong?”)
ip_in_vlan	Check if an IP belongs to a VLAN; if not, provide a best-guess VLAN match (when unique)
plan_subnets	Allocate VLAN subnets from a parent IPv4 block (deterministic)

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

Tool	Description
rdap_lookup	WHOIS-style lookup using RDAP for domains and IPs
asn_lookup	Origin ASN lookup for an IP (BGP origin intel)

Connectivity Tools

Tool	Description
ping	ICMP ping with latency statistics and packet loss
traceroute	Path analysis showing each hop with latency
dns_lookup	DNS resolution (A, AAAA, MX, TXT, etc.) and reverse lookups
port_check	TCP port connectivity test with banner grabbing
mtr	Combined traceroute + ping with per-hop statistics

Batch Operations

Tool	Description
batch_ping	Ping multiple hosts concurrently
batch_port_check	Check multiple ports on a single host
batch_dns_lookup	Resolve multiple hostnames in parallel

Local Network Info Tools

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

Tool	Description
get_interfaces	List network interfaces with IPs, MACs, and status
get_routes	Get routing table with default gateway
get_dns_config	Get configured DNS servers and search domains
get_arp_table	Get ARP cache (IP to MAC mappings)
get_connections	List active TCP/UDP connections
get_public_ip	Get public/external IP address as seen from the internet

Pcap Analysis Tools

Tool	Description
pcap_summary	High-level capture stats: packets, duration, protocols, top talkers
get_conversations	Network flows/conversations between endpoints
analyze_throughput	Observed throughput (Mbps) per conversation/flow, including dominant direction and duration
find_tcp_issues	Detect retransmissions, resets, zero windows, dup ACKs
analyze_dns_traffic	DNS queries, failures, slow responses
filter_packets	Extract packets by IP, port, or protocol
get_protocol_hierarchy	Protocol breakdown by packets and bytes
custom_scapy_filter	Execute custom scapy filter expressions

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:

Example	Description
ollama_agent.py	Interactive chat agent using Ollama (local models)
incident-demo/	Self-contained demo: AI diagnoses network outages with voice alerts
eval_agent.py	Evaluate how well models use the network tools

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.