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.

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

Location	Country	Latency (avg)	Packet Loss	Performance Rating
Seattle, US	US	~1-4ms	0%	⭐⭐⭐⭐⭐ Excellent
Switzerland	CH	~1ms	0%	⭐⭐⭐⭐⭐ Excellent
Singapore	SG	~2.1ms	0%	⭐⭐⭐⭐⭐ Excellent
Sydney, Australia	AU	~0.54ms	0%	⭐⭐⭐⭐⭐ Outstanding
Campo Grande, Brazil	BR	~19.8ms	0%	⭐⭐⭐ Good

🔍 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:

Prioritize APAC: Australia and Singapore show excellent performance
European Expansion: Switzerland performance suggests good connectivity
Brazil Improvement: Higher latency indicates need for local presence

Strategic Recommendations:

Multi-CDN Strategy: Deploy edge servers in Sydney, Singapore, and Europe
Peering Optimization: Establish direct peering with major ISPs in Brazil
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

Install dependencies
Configure your MCP client to use Looking-Glass-MCP
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.

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How are these scores calculated?

A Model Context Protocol server that provides network probing capabilities through Looking Glass vantage points, allowing users to perform global network diagnostics like ping, BGP route lookups, and traceroute operations from multiple locations worldwide.

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