Model Context Protocol (MCP) Servers are lightweight service processes that expose tools, resources, and prompt templates to AI agents over a standardised JSON-RPC interface. Introduced by Anthropic in 2024, MCP defines a client-server architecture in which an AI host application (the MCP client) connects to one or more MCP servers, each of which advertises a set of callable functions. The model can then invoke those functions during inference and incorporate their results into its context.

Think of it like a USB standard for AI capabilities. Instead of every AI application hard-wiring integrations to databases, file systems, or web APIs, an MCP server wraps each integration and any compliant host can plug in.

A developer building a coding assistant might run an MCP server that exposes tools such as read_file, run_tests, and search_docs. The LLM calls these tools mid-conversation, receives structured results, and continues reasoning. The server handles authentication and execution; the model handles interpretation. MCP servers can be local processes or remote HTTP endpoints, and multiple servers can be connected simultaneously.

MCP sits at the intersection of Tool-Use Reasoning and the Operator Pattern, enabling structured multi-step workflows. It extends the effective Context Window of a model by letting it pull in fresh data rather than relying solely on training knowledge, which helps mitigate Hallucination. The protocol is model-agnostic: any LLM whose host implements the MCP client spec can use the same servers, similar to how Transformer architectures can swap Attention implementations. Fine-tuning is not required; the model interacts through Prompt Engineering and structured tool definitions.

You will encounter MCP servers when building agent systems, configuring Claude Desktop or Claude Code, or integrating enterprise data sources into LLM workflows.