Building an MCP Server for PCAP Analysis — Looking for Architecture & Best Practice Suggestions

[Khushboo1910]

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Hello Experts,

I’m planning to build an MCP (Model Context Protocol) server focused on PCAP/network traffic analysis and would love input from the community. I have gone through this article but I still felt there might be an improvement areas. Link- https://skywork.ai/skypage/en/wireshark-mcp-server-guide-ai-engineers/1980151768098840576

The goal is to create an MCP server that allows an LLM to intelligently analyze .pcap files, inspect protocols, detect anomalies, and assist with troubleshooting/security investigations.

I’m currently designing the architecture and trying to identify:

1. Core MCP Tools
   What are the ideal tools/functions an MCP server for PCAP analysis should expose?

Some ideas:

analyze_pcap() → protocol summary, conversations, statistics

detect_anomalies() → suspicious traffic patterns

live_capture() → real-time interface capture

2. Resources
   What resources should ideally be exposed to the LLM?

3. Special Prompts
   What instructions are important for safe and accurate analysis?

4. Best Practices
   Looking for recommendations around:

a. MCP architecture patterns
b. Tool granularity (small tools vs large tools)
c. Performance optimization for large PCAPs
d. Streaming analysis workflows
e. Security considerations
f. Multi-agent approaches for protocol analysis
g. Best way to expose tshark functionality safely
h. Handling token/context limitations with large captures

If anyone has built something similar — especially around Wireshark, tshark, MCP-based security tooling — I’d really appreciate your insights, architecture ideas, or open-source references.

Note: Further, I can use this MCP tool to analyze logs from various network security tools such as Sysinternals, NetMon, ProcMon, etc. Please also add your comments on whether this can be achieved using a single MCP server.

Thanks!

[YahyaMansoub]

A good headstart would be to analyze this project https://github.com/weirdmachine64/SharkMCP , it's a great reference .

[tathagat22]

I've built a couple of MCP servers (one that distills web content down for agents, one for design extraction), and what you're describing is the same problem I kept running into: the artifact is way bigger than the context window, so the whole architecture is really about deciding what not to send.

Some opinions, in rough order of how much they matter:

Don't build analyze_pcap(). One big tool means you're guessing server-side about what the model needs. Split it into stages and let the model drill down itself:

• a summary tool: capinfos-style metadata + protocol hierarchy (-z io,phs) + top conversations (-z conv,tcp). That's a few hundred tokens and enough for the model to form a hypothesis
• a filter tool that takes a display filter and returns stream/conversation-level stats, not packets
• a drill-down tool for a single stream (-z follow,tcp,ascii,N) with a hard cap on bytes returned

Give anything with variable output an explicit token/size budget parameter and enforce it server-side. And when you truncate, say so in the output ("showing 40 of 3,209 matching packets"). Silent truncation is poison — the model will confidently conclude the traffic is clean because you never showed it the bad part.

Cache derived results, keyed on (pcap hash, filter). Agents re-ask the same question constantly; the second identical call should cost nothing.

tshark safety is mostly mundane: spawn with an argv array, never through a shell; allowlist the -z statistics you actually support rather than passing flags through; cap output size and wall time. The display filter is the one user-controlled string you can't avoid, but tshark validates it for you — it exits non-zero on bad filter syntax — so running it against a tiny bundled pcap first is a cheap validity check.

live_capture(): leave it out of v1. A running capture is state, and request/response tools model state badly. If you need it later, do start/status/stop returning a handle. Honestly, an LLM analyzing a static pcap is already useful; live capture adds a lot of failure modes for marginal gain.

Resources vs tools: put the raw pcap and large derived artifacts (the full conversation table, say) behind resources, and keep tools for the distilled views. Resources are opt-in for the client; a tool should never surprise anyone with 200k tokens.

ProcMon/NetMon/Sysinternals in the same server: yes, that works. The summarize → filter → drill-down pattern is identical, only the parsers differ. Namespace the tools (pcap_*, procmon_*) and watch the total count — past ~20 tools, clients start picking the wrong one.

On prompts: the most useful instruction I've found is telling the model to state a hypothesis before drilling down, and to treat absence of evidence in a truncated view as "unknown", not "clean". That one line prevents most of the overconfident wrong conclusions.

The SharkMCP repo linked above is worth reading for the tshark wrapping itself, though I'd push much harder on the token budgeting than the existing wrappers do.