You do not need an integration per AI tool. The Model Context Protocol is an open standard, so one MCP server makes your company knowledge available in Claude, in Cursor, and in any other client that speaks MCP — one build, one URL, every surface your team works in. On SuperCognit that build takes no code: import your website, upload documents, publish.
The matrix nobody builds
Before a standard, connecting knowledge to tools was a multiplication problem: every source times every tool, each pair its own integration project. That matrix is why company knowledge never made it into anyone's assistant — not because it was hard in principle, but because nobody staffs M times N integration projects for material that lives in a wiki. A protocol turns the multiplication into addition: the server is built once, and every client that implements the standard connects to the same endpoint. The economics of the whole problem change shape, which is usually what a good standard does.
'Every tool' is doing real work in that sentence
Teams do not standardise on one AI client, and they should not have to. Support lives in Claude. Engineers live in Cursor. Someone will adopt a client next quarter that does not exist today. A per-tool integration strategy has to predict those choices; an MCP server does not care about them. Whatever your people use next, if it speaks the protocol, your knowledge is already there.
The colleagues who will never install anything
Not everyone runs an MCP client, and a knowledge strategy that only serves the technical half of the company is half a strategy. On SuperCognit the same build that publishes the MCP server also produces a chat agent, so the operations manager who will never edit a client configuration asks the same questions against the same knowledge in a browser. One source of truth, two doors in.
One place to be wrong
The quiet benefit is governance. When pricing changes, you change it once — the imported source re-syncs and every connected client answers from the new version after the next scheduled pull. Compare that with the five copies of the old pricing sheet living in five people's chat histories, each pasted in good faith weeks ago. Answers also cite their sources, so when something looks off, the trail leads to a page someone can fix, not to a paste nobody can trace.
Private or public, same mechanics
The server can be private — an internal brain, gated per seat — or public, if the knowledge is meant for the world: a boutique hotel putting its rates, policies and local know-how within reach of any guest's assistant works exactly the same way. You can even sell access per seat, with subscriptions and invoicing built in. The distribution decision is a setting, not a rebuild.
Getting there
Import your website — it is crawled into a cited knowledge base in about two minutes — and upload the documents that never made it online. Publish. Hand the URL to whoever wants it in their client; OAuth 2.1 handles who gets in, per seat, and your workspace is isolated from every other tenant.
The strategic point is small but durable: standards remove the obligation to predict winners. You do not have to know which AI client your company will run in two years. You have to know your own knowledge — and put it somewhere all of them can reach.