MCP
MCP (Model Context Protocol) is an open standard for connecting AI agents to external tools. An MCP server exposes tools and resources; an MCP-compatible agent calls them as if they were local. It is the cleanest way to extend an agent.
Why it matters
Before MCP, every AI agent product had its own way of plugging in custom tools. Some shipped as JSON-schema function definitions baked into a vendor SDK. Some required a plugin manifest hosted on a specific path. Some were "actions" stored inside the vendor's own dashboard. Each approach worked, but only inside that vendor's ecosystem; tools you wrote for one assistant did not run on another. This was a 2023-2024 problem and it shaped how slowly the AI agent layer matured.
MCP is the open standard that solved it. The protocol is small, the specification is public, and adoption is wide enough that an MCP server you write today runs against Claude, Cursor, Zed, Ajeris, and a growing list of other clients. From a developer's view, you write a small server that lists tools and answers tool calls. From an agent's view, you connect a URL or a stdio command and the agent suddenly has new capabilities.
For a person building an AI agent product, MCP is also a forcing function on architecture. It pushes you to keep tools small, well-typed, and self-describing. The agents that use MCP cleanly tend to behave better than the ones whose tools grew organically without that structure.
How Ajeris handles this
Ajeris is MCP-native on both sides of the protocol. On the agent side, the built-in toolbox is exposed as MCP servers (memory, gmail, calendar, meeting, finance, business, and so on). On the user-facing side, you can bring your own MCP server by pasting in a URL or a stdio command; the agent picks up the new tools on the next turn. This means an integration we have not built yet (Salesforce, Asana, your internal admin tool) is something you can wire in yourself without waiting on us. See the docs for the full guide.
