# Looking Glass

> Use this tool when you need to perform network probing and diagnostics from multiple global vantage points, solving problems such as connectivity analysis, BGP route investigation, and network troubleshooting. It takes inputs like user-defined VP selection, quantity requirements, and query types, and outputs results from ping, traceroute, and BGP queries. Use Looking Glass in contexts where programmatic access to distributed network measurement capabilities is required, such as building AI assistants for network diagnostics and optimization.

Canonical page: https://skillsregistry.net/skills/jackie-shi-looking-glass  
JSON: https://api.skillsregistry.net/v1/skills/jackie-shi-looking-glass

## Description

This MCP server provides network probing capabilities through Looking Glass (LG) vantage points, enabling AI assistants to perform ping, traceroute, and BGP queries from distributed network locations worldwide. Built by Jackie-shi using Python with FastMCP and httpx, it offers three core tools: user-defined VP selection for targeted probing from specific network locations, automatic VP selection where the platform chooses optimal vantage points based on quantity requirements, and comprehensive LG listing to discover available network measurement points. The implementation connects to a local API service running on localhost:44332 that manages the actual network probing operations, includes a comprehensive database of LG endpoints with their specific command formats and ASN information covering major ISPs and hosting providers globally, and features robust error handling with extended timeouts for network operations, making it valuable for network troubleshooting workflows, connectivity analysis, BGP route investigation, and building AI assistants that need programmatic access to distributed network measurement capabilities without manual Looking Glass interface navigation.

## Trust

- **Trust score (0–1):** 0.99
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-09-28

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/jackie-shi-looking-glass)
- **Repository:** <https://github.com/jackie-shi/looking-glass-mcp>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "jackie-shi-looking-glass"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/jackie-shi-looking-glass` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/jackie-shi-looking-glass/pull`

---
SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt