Wireshark Mcp MCP Server

[giscus[bot]]

Wireshark Mcp MCP Server

Give your AI assistant a packet analyzer.
Drop a .pcap file, ask questions in plain English — get answers backed by real tshark data.

https://mcp.so/server/wireshark-mcp/bx33661?tab=comments

[bx33661]

🙂

[azmartone67]

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[SonAIengine]

been debugging network stuff with raw tshark and this would've saved me hours. natural language layer for filter queries is clever — though I'd worry about context limits w/ really noisy captures. tested on anything with huge packet counts?

[bx33661]

Great question — context limits are definitely a real concern we thought about. The design handles it in a few ways:

1. tshark does the heavy lifting, not the LLM.
   The “natural language layer” simply translates your intent into a display filter (e.g., http.response.code == 200), and then tshark executes it natively on the capture file. The AI never sees the raw packets — it only receives the filtered, bounded output.

2. Built-in pagination and limits on every read tool.
   wireshark_get_packet_list has a hard limit + offset, so you can paginate through millions of packets without ever blowing the context window. Stream following works the same way — when truncated it returns something like:
   [N more lines available, use offset=X].

3. Stats tools aggregate before returning results.
   For large captures, tools like wireshark_quick_analysis, wireshark_stats_conversations, and wireshark_plot_traffic run -z statistics internally. That means you get compact summaries (hundreds of bytes) instead of raw packet dumps (which could be gigabytes).

We’ve tested this on captures in the 100k–200k packet range without issues. It has also been extensively tested in CTF competitions, where it performed very well in practice.

For extremely noisy captures (millions of packets), the recommended workflow is still:

Run a stats / conversation tool first to triage.

Then drill down into specific streams or filtered subsets.

That said, we have to admit that this approach still consumes a fair amount of tokens. However, compared to feeding raw packet data directly to the model, it keeps the context usage much more manageable.

Happy to hear if you run into any edge cases — that kind of feedback is exactly what helps improve the tool.