Blog

Major AI coding assistants differ significantly in IDE support, model flexibility, MCP integration, and pricing models

An MCP server is a process that exposes tools, resources, and prompts over a standard transport (stdio or HTTP)

Docker-based MCP servers provide better isolation and security compared to globally installed npm or pip packages

MCP database servers let AI assistants query your databases directly, but read-only mode should be the default for produ...

Production MCP deployments require treating servers as trusted processes with real system access — not as sandboxed chat...

Most MCP failures are config file syntax errors, wrong file paths, or missing environment variables

MCP is designed for AI consumption — tools are self-describing and discoverable at runtime without external documentatio...

Claude Code reads MCP server configuration from a JSON file in your project or home directory

MCP adoption has become a mainstream expectation — most serious AI coding tools now support it or are actively adding it

MCP servers extend AI assistants with real capabilities like file access, GitHub integration, and web browsing

MCP configuration errors are the most common cause of server connection failures in Claude Desktop and other clients

Local MCP servers run on your machine via stdio and are ideal for filesystem access, development tools, and sensitive da...

MCP servers can access local files, databases, and APIs — always review permissions before installing

MCP (Model Context Protocol) is an open standard for connecting AI applications to external data sources, tools, and wor...

Cursor supports MCP servers through its built-in configuration, enabling AI-powered tool access