The Model Context Protocol (MCP) is an open standard, introduced by Anthropic, for connecting AI applications to external tools and data sources through a single uniform interface. Instead of every assistant building bespoke integrations for every system, an MCP server exposes tools, resources, and prompts that any MCP-compatible client can call — a common plug between models and the systems they need to reach.
Its value is interoperability. Before a shared protocol, each pairing of an AI app with a data source was a custom connector; MCP collapses that many-to-many problem into a standard both sides implement once. A growing ecosystem of servers — for filesystems, databases, issue trackers, and SaaS APIs — means a capable client gains access to all of them without new integration work.
It is worth being precise about what MCP does and does not solve. MCP connects an agent to tools and data; it does not, by itself, decide which knowledge an agent needs before changing a specific part of a codebase. Retrieval through a protocol is plumbing; knowing that a given file carries a non-obvious invariant is context. The two are complementary — a protocol moves information, a context layer determines which information is the right information.
Sources: modelcontextprotocol.io ↗