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Network MCP Server

by labeveryday
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Python
Remote

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.

PyPI version Python 3.10+ License: MIT

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

Related MCP server: WireMCP

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.

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