An MCP server for analyzing PCAP files.
An MCP server for analyzing PCAP files. That is what the mcpcap mcp server brings to an AI assistant: the same capability, reachable through the Model Context Protocol rather than a separate app or dashboard.
A modular Python MCP (Model Context Protocol) server for analyzing PCAP files. mcpcap exposes protocol-specific analysis tools that accept a local file path or remote HTTP URL at call time, so the server stays stateless and works cleanly with MCP clients.
mcpcap uses a modular architecture to analyze different network protocols found in PCAP files. Each module provides specialized analysis tools that can be called independently with any PCAP file, making it perfect for integration with Claude Desktop and other MCP clients.
@modelcontextprotocol/inspector on npm is all you need. Most clients run it directly, so configuration is a few lines and a restart.
This sits in the AI and media services group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. It is maintained by ai.mcpcap; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
{
"mcpServers": {
"mcpcap": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/inspector"]
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.