What is MCP?
The Model Context Protocol (MCP) is an open standard that lets AI models and AI agents connect to external tools and data in a consistent, secure way. Instead of hard-coding one-off integrations, you run or connect to an MCP server that advertises what it can do—query a database, read files, call an API, fetch documents—and an MCP-compatible client (an agent runner, IDE, or chat app) discovers and uses those capabilities.
The result is less glue code, cleaner security boundaries, and portable agent skills you can reuse across projects and model providers.
Teams adopt MCP because it separates concerns. Tooling lives behind the protocol with its own auth and permissions; the LLM focuses on reasoning and deciding which tool to call; and orchestration layers enforce policies, rate limits, and logging. When you add a new capability—say, a vector search index or a ticketing system—you expose it once via MCP, and every compliant client can discover and invoke it without bespoke SDKs. This also improves observability: you can record which tools were called, with what arguments, and what evidence was returned.
At a high level, an MCP client connects to one or more servers, lists available capabilities, and then invokes them with structured inputs. Servers can serve “resources” (documents, files), “tools” (actions with parameters and results), and shared prompts or templates.
Access is permissioned: admins decide which capabilities are visible, what data paths are allowed, and which actions require approvals. Because the interface is standardized, you can swap underlying systems—move from one vector database to another, or from a staging to a production API—without rewriting your agent’s logic.
What MCP standardizes in practice: capability discovery (what tools/resources exist), structured tool calls and results, access controls and sandboxing, and auditable events/streams so you can trace what an agent did and why.
Example
A retailer deploys a “catalog fixer” agent to clean up missing attributes. The agent connects to three MCP servers: a product database server that exposes read/write queries, a media server that serves product photos, and a workflow server that creates review tickets.
Given “Fill color and material where missing,” the agent discovers the available tools, pulls SKUs with gaps, uses vision and text extraction to infer attributes, writes updates back with evidence links, and opens tickets for low-confidence cases. Later, the team swaps the vector store behind the scenes and adds a PIM API—no agent rewrite, just new MCP capabilities. Every step is permissioned and logged, so changes are traceable and safe.