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: WireMCP

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.