Looking-Glass-MCP

The first Looking Glass Model Context Protocol (MCP) server! 🎉

Overview

Looking-Glass-MCP is a revolutionary MCP server that provides network probing capabilities through Looking Glass (LG) vantage points. This tool allows you to perform network diagnostics and measurements from multiple global locations using a simple, standardized interface.

Related MCP server: Domain Tools MCP Server

Features

• Multi-VP Probing: Execute network commands from multiple Looking Glass vantage points simultaneously

• Auto VP Selection: Automatically select the optimal number of vantage points for your measurements

• Comprehensive Commands: Support for ping, BGP route lookups, and traceroute operations

• Global Coverage: Access to Looking Glass servers worldwide

• Async Operations: Built with async/await for efficient concurrent operations

• Error Handling: Robust error handling and timeout management

Available Tools

lg_probing_user_defined

Send probing commands to a target IP using a specific list of LG vantage points.

Parameters:

• vp_id_list: List of Looking Glass VP identifiers

• cmd: Command type (ping, show ip bgp, traceroute)

• target_ip: Destination IP address for probing

lg_probing_auto_select

Send probing commands using automatically selected vantage points.

Parameters:

• vp_num: Number of vantage points to use

• cmd: Command type (ping, bgp, traceroute)

• target_ip: Destination IP address for probing

list_all_lgs

Retrieve information about all available Looking Glass vantage points.

Requirements

• Python 3.13+

• httpx >= 0.28.1

• mcp[cli] >= 1.9.4

Installation

pip install -r requirements.txt

Usage Example: CDN Performance Analysis

This example demonstrates how to use Looking-Glass-MCP for CDN performance optimization by analyzing network performance to Google's DNS service (8.8.8.8) from multiple global locations.

Step 1: List Available Vantage Points

# Get all available Looking Glass vantage points
result = await list_all_lgs()

Step 2: Select Global Vantage Points

We selected 5 diverse global locations for comprehensive analysis:

• US (Seattle): VP ID 71 - North America

• Switzerland: VP ID 164 - Europe

• Singapore: VP ID 62 - Asia-Pacific

• Australia (Sydney): VP ID 125 - Oceania

• Brazil (Campo Grande): VP ID 108 - South America

Step 3: Execute Network Probing

# Run ping tests from 5 global locations
ping_results = await lg_probing_user_defined(
    vp_id_list=["71", "164", "62", "125", "108"],
    cmd="ping",
    target_ip="8.8.8.8"
)

# Run traceroute analysis
traceroute_results = await lg_probing_user_defined(
    vp_id_list=["71", "164", "62", "125", "108"],
    cmd="traceroute", 
    target_ip="8.8.8.8"
)

# Analyze BGP routing information
bgp_results = await lg_probing_user_defined(
    vp_id_list=["71", "164", "62", "125", "108"],
    cmd="bgp",
    target_ip="8.8.8.8"
)

Step 4: LLM-Generated Analysis Results

📊 Example Output: The following analysis was automatically generated by an LLM using the Looking-Glass-MCP tool results. This demonstrates how AI can transform raw network data into actionable business insights.

🌍 Global Network Performance Summary

🔍 Key Findings

Best Performance:

• Australia (Sydney): Exceptionally low latency (~0.54ms) - direct Google peering

• Switzerland & US: Sub-millisecond to low single-digit latency

Routing Analysis:

• Australia: 6 hops, direct peering with Google infrastructure

• Singapore: 4 hops, excellent Google edge presence

• Switzerland: 4 hops, optimal European routing

• Brazil: 15+ hops, complex ISP routing causing higher latency

BGP Intelligence:

• All locations show AS15169 (Google) as origin

• Multiple path redundancy visible

• Local peering arrangements in major markets

🎯 CDN Optimization Recommendations

Immediate Actions:

1. Prioritize APAC: Australia and Singapore show excellent performance

2. European Expansion: Switzerland performance suggests good connectivity

3. Brazil Improvement: Higher latency indicates need for local presence

Strategic Recommendations:

1. Multi-CDN Strategy: Deploy edge servers in Sydney, Singapore, and Europe

2. Peering Optimization: Establish direct peering with major ISPs in Brazil

3. Performance Monitoring: Use these 5 locations as baseline monitoring points

Expected Impact:

• Australia/Singapore: Maintain sub-2ms response times

• Europe: Target sub-5ms response times

• Brazil: Improve from 20ms to <10ms with local presence

💡 Key Insight: This example shows how Looking-Glass-MCP enables AI assistants to automatically analyze complex network data and provide actionable business recommendations - transforming raw technical metrics into strategic insights.

Real-World Applications

This Looking Glass MCP tool is perfect for:

• CDN Performance Optimization: Analyze global performance patterns

• Network Troubleshooting: Identify routing issues from multiple perspectives

• DDoS Detection: Monitor traffic patterns across vantage points

• Competitive Analysis: Benchmark against competitor infrastructure

• SLA Monitoring: Validate service level agreements globally

• Research: Academic studies on internet topology and performance

Getting Started

1. Install dependencies

2. Configure your MCP client to use Looking-Glass-MCP

3. Start analyzing global network performance!

The power of Looking Glass combined with MCP's standardized interface makes network analysis accessible and actionable for any application requiring global network intelligence.