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MCP Ping Server

MCP Server

Instant network connectivity checks for Claude Desktop

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Updated 22 days ago

About

A lightweight, cross‑platform MCP server that enables Claude Desktop to perform ping operations and quick internet connectivity tests via asynchronous FastMCP and SSE.

Capabilities

Resources
Access data sources
Tools
Execute functions
Prompts
Pre-built templates
Sampling
AI model interactions

Claude desktop

The MCP Ping Server is a lightweight, cross‑platform tool that plugs directly into Claude Desktop via the Model Context Protocol (MCP). Its primary purpose is to give an AI assistant instant access to network diagnostics, enabling developers to verify connectivity, troubleshoot latency issues, or confirm that a target host is reachable—all without leaving the conversational UI.

At its core, the server exposes two simple yet powerful tools. lets Claude send ICMP echo requests to any hostname or IP address, with a configurable packet count that defaults to four. This is ideal for gauging round‑trip times or detecting packet loss in real time. performs a quick reachability test against Google’s public DNS server (8.8.8.8), providing a single‑shot confirmation that the machine running Claude has internet access. Because both tools run asynchronously under FastMCP and Starlette, the server can handle multiple concurrent requests without blocking the AI’s workflow.

Developers benefit from this integration in several ways. In debugging sessions, Claude can automatically ping a server before suggesting configuration changes, ensuring that network conditions are understood in context. During onboarding or training of new systems, the assistant can verify connectivity across different environments—Windows, Linux, macOS—without manual intervention. The server’s Server‑Sent Events (SSE) interface streams results back to Claude in real time, allowing the assistant to present live updates and even stream diagnostic logs directly into the conversation.

The MCP Ping Server stands out for its simplicity, minimal dependencies, and secure design. It is intended for local use, with a clear warning against exposing it publicly, which keeps the risk surface low. By embedding basic network checks into the AI workflow, developers can streamline troubleshooting pipelines and reduce context‑switching, making the assistant a more effective partner in day‑to‑day operations.