Wireshark MCP Server

Wireshark MCP Server – Overview

The Wireshark MCP Server bridges the gap between AI assistants and network packet analysis by exposing PyShark’s capabilities through the Model Context Protocol (MCP). In practice, this means an assistant such as Claude can now request live packet captures, query historical captures, or analyze traffic patterns directly from the user’s environment. By turning a traditionally command‑line tool into an AI‑friendly API, developers can embed deep network diagnostics into conversational workflows without requiring users to manually run Wireshark or TShark.

At its core, the server offers a set of resources that provide static information about the PyShark installation—version details, configuration settings, and a history of previous captures. These resources give the assistant quick context about the underlying environment, allowing it to adapt its queries or troubleshoot compatibility issues. The tools layer is where the real power lies: from listing all available network interfaces to capturing targeted traffic, reading pcap files, and performing protocol‑specific analyses. Advanced tools such as , , and enable fine‑grained control, making it possible to isolate a single host or protocol for detailed inspection.

Developers benefit from this server in several tangible ways. First, it eliminates the need to write custom wrappers or scripts for packet capture; instead, they can rely on a standardized MCP interface that any AI assistant can call. Second, the server’s history management allows assistants to reference past captures, facilitating longitudinal studies or trend analysis. Third, the built‑in prompts (e.g., ) provide context‑aware guidance to users, reducing the learning curve associated with network diagnostics.

Typical use cases include security incident response, where an analyst can ask the assistant to capture traffic from a suspect IP for a fixed duration and then receive an automated summary of suspicious patterns. In performance engineering, developers might request live HTTP traffic captures to identify latency bottlenecks or malformed requests. Educational environments can also leverage the server, letting students interactively learn about network protocols by asking the assistant to filter and display specific traffic types.

Integration with AI workflows is seamless: once installed in a client like Claude Desktop, the assistant can invoke any tool as part of its response generation. The server’s MCP endpoints return structured data that the assistant can embed directly into chat messages, ensuring a fluid conversational experience. Because MCP handles authentication and error reporting internally, developers can focus on crafting higher‑level prompts rather than managing low‑level capture logistics.

In summary, the Wireshark MCP Server turns complex packet analysis into an AI‑driven service. By exposing PyShark’s full feature set through a clean, standardized protocol, it empowers developers to build richer, more interactive diagnostic tools that can be accessed conversationally from any MCP‑compatible assistant.