DevEnvInfoServer

Overview

The DevEnvInfoServer is a Cursor Model Context Protocol (MCP) server designed to expose a comprehensive snapshot of a developer’s local environment. By aggregating data from the operating system, package managers, virtual environments, and installed tooling, it supplies an AI assistant with a granular view of the machine’s configuration. This enriched context enables assistants to tailor suggestions, diagnose issues, and generate code that is tightly aligned with the user’s actual setup.

The server collects a wide range of information: from low‑level system metrics such as CPU load, battery status, and network ports, to high‑level development artifacts like installed compilers, language runtimes, IDE extensions, and active Docker or Kubernetes clusters. It also surfaces environment variables, shell configuration files, Git settings, and cloud SDKs. By presenting this data in a structured MCP format, the assistant can quickly answer questions such as “Which Python virtual environment is active?” or “What version of Node.js is available on the PATH?” without requiring manual inspection.

Key capabilities include:

• System discovery – OS, architecture, locale, and firewall status.
• Toolchain inventory – compilers (gcc/clang), interpreters, and language runtimes.
• Package manager introspection – listings from brew, npm, pip, conda, and more.
• Environment profiling – active virtual environments, shell aliases, and dotfiles.
• Performance metrics – CPU load, memory usage, GPU utilization, and temperature sensors.
• Network & security overview – open ports, VPN/proxy settings, and SSH keys.
• Container & virtualization status – Docker, WSL, Vagrant, and virtual machine hosts.

Developers benefit from this server in several real‑world scenarios. When configuring a new project, an assistant can recommend the correct interpreter or suggest missing dependencies by querying the server’s package lists. During debugging, it can surface active debugger sessions or system performance bottlenecks that might affect execution. In CI/CD pipelines, the server can validate that required tools (e.g., Terraform, Helm) are present before a build is triggered. For remote or cloud development, the assistant can expose active SSH configurations and cloud SDK versions to ensure seamless connectivity.

Integrating the DevEnvInfoServer into an AI workflow is straightforward: a Cursor client requests specific resource types (e.g., “system_info” or “python_packages”), and the server responds with JSON payloads that the assistant can consume. Because the data is refreshed on demand, developers always receive up‑to‑date information without manual caching. This tight coupling between environment introspection and AI reasoning empowers assistants to act as true “smart pair programmers,” delivering context‑aware guidance that adapts to the nuances of each developer’s setup.