The Ghost in the Machine Is Finally Allowed to See: A Beginner's Guide to MCP (danielkliewer.com)

The Model Context Protocol (MCP) is an open standard that lets large language models interact safely and directly with developer tools—filesystems, git repos, indexed docs and even shells—so the model can “see” and act on real project context instead of guessing from pasted snippets. Practically, you enable it by installing Anthropic’s MCP VS Code extension (code --install-extension anthropic.mcp) and running one or more MCP servers (examples: npm install -g @context7/mcp-server with config at ~/.config/mcp/servers/context7.json; pip install mcp-filesystem; npm install -g @mcp/git-server; pip install mcp-shell-server). Once running, models like Claude Desktop, GPT-4, Ollama or Grok can be queried with structured commands—@context7 search "refreshToken logic", @filesystem read src/lib/auth.js, @git commit "…", @shell "npm test"—providing searchable, index-backed access and guarded action capabilities. For AI/ML practitioners this is a substantive shift: MCP reduces hallucinations by giving models authoritative access to code, history and docs, enables end-to-end “vibe-coding” workflows (discover, read, patch, test, commit) and decouples model quality from contextual capability—any model can leverage the same live project view. Technical implications include improved reproducibility and provenance (model sees repo history), lower friction in onboarding and debugging, and a need to treat MCP endpoints as guarded attack surfaces (shell actions have safety rails). In short, MCP turns LLMs from guessers into context-aware collaborators, changing how developers and models cooperate.