What is MCP?
MCP is a protocol pattern for connecting AI clients to tools, data sources, and external capabilities in a predictable way.
MCP definition
MCP stands for Model Context Protocol. In practical terms, it gives an AI client a standard way to discover what a server can do, call those capabilities, and receive structured results that the model can use.
Without a protocol layer, every tool integration becomes a custom adapter. With MCP, the client and server agree on a shared shape for discovery, invocation, permissions, inputs, outputs, and errors.
Why MCP matters
Clients can connect to many capabilities without every integration becoming a one-off implementation.
Servers expose specific capabilities instead of giving an agent uncontrolled access to a whole system.
Tool names, schemas, results, and errors become easier to log, test, and reason about.
Core parts
The agent runtime or app that discovers available servers and decides when to call a capability.
The service or local process that exposes tools, resources, prompts, or domain-specific actions.
A callable tool or readable resource with a name, description, input schema, output shape, and failure behavior.
The structured response that the client returns to the model so it can continue the task.
Typical flow
The basic MCP workflow is discovery, selection, invocation, execution, and result handling.
AI client ββ discovers capabilities ββ> MCP server AI client ββ sends structured input ββ> capability MCP server ββ executes safely ββββββββ> internal system or data source AI client <β receives result/error βββ MCP server Agent ββ uses result in task βββββ> final answer or next action