Model Context Protocol (MCP)
MCP is an open protocol, now under Linux Foundation governance, that standardizes how AI models and agents connect to external tools and data sources — an app can expose an "MCP server" that any compliant AI client can query.
Before a shared protocol like MCP, connecting an AI assistant to a piece of software meant a bespoke integration for every pairing — a plugin for this chat tool, a connector for that CRM, none of it reusable. MCP standardizes the interface instead: a system exposes an MCP server describing what data and actions are available, and any MCP-compliant AI client (an assistant, an IDE, an agent) can discover and use it the same way, without a custom integration per pairing.
The protocol has grown quickly because it solves a real N×M problem: without a standard, every AI tool needs a custom connector to every data source. With one, a single MCP server implementation works with any client that speaks the protocol — reported SDK download volume is now in the tens of millions per month, and it's governed by the Linux Foundation rather than a single vendor.
Being "MCP-native" for a product usually means two things: acting as a server (so external AI tools can query the product's data under proper access control) and acting as a client (so the product's own AI features can call out to other MCP servers). Both directions matter — one makes a product's data usable by AI elsewhere, the other makes external tools usable by AI inside the product.
Model Context Protocol (MCP), in the product
Aanty ships an MCP server, exposing your conversation graph to Claude, Cursor, ChatGPT and other MCP clients under your existing ACLs — and aanty is also an MCP client, so agents inside aanty can call any MCP server in turn. It's part of the no-API-lockdown pledge: your graph is queryable by the AI you choose.
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