The promise of MCP is "build once, connect everywhere," and this is the post where that gets concrete. If your knowledge base speaks MCP, the same server plugs into Claude, into Cursor, into Windsurf, and into any other MCP-compatible client — no per-tool integration code. Your team's AI tools gain the ability to search your knowledge and ground their answers in it. Here's how the connection actually works, and how to set it up.
The one-server, many-clients model
The whole point of MCP is that the server (your knowledge base) and the clients (the AI tools) speak a shared protocol, so they connect without bespoke glue. You expose your knowledge base as an MCP server once — with tools like search_knowledge_base and get_document — and then any MCP-compatible client can be pointed at it and immediately use those tools. Claude, Cursor, and Windsurf are all MCP clients, so all three connect to the same server the same way. Add a new MCP client next year and it works too, with zero extra work on your side.
With Kognita, every knowledge base ships an MCP server out of the box, so there's nothing to build — you're just connecting clients to an endpoint that already exists.
What you need
Before wiring up any client, you need two things from your MCP server:
- The server endpoint — the URL (or command) the client connects to.
- Credentials — how the client authenticates. For anything beyond a local prototype, this should be proper scoped authorization rather than a bare secret, so the client is granted exactly the access it needs (typically read/search on your knowledge base).
Keep these handy; each client's setup is essentially "point at the endpoint, provide the credentials."
Connecting Claude
Claude supports MCP servers through its connector/configuration settings. The pattern:
- Open Claude's MCP/connector configuration.
- Add your knowledge base MCP server — its endpoint and credentials.
- Claude discovers the tools the server exposes (e.g.
search_knowledge_base) and makes them available in conversation.
Now, when you ask Claude something your knowledge base covers, it can call the search tool, retrieve the relevant passages, and answer grounded in your content — with the option to cite where the answer came from. The knowledge base becomes a capability Claude has, not a place you separately go look things up.
Connecting Cursor and Windsurf
Cursor and Windsurf are AI-native code editors, and both support MCP servers through their settings/configuration. The setup mirrors Claude:
- Open the editor's MCP configuration (typically in settings).
- Register your knowledge base MCP server with its endpoint and credentials.
- The editor's AI assistant gains access to the server's tools.
The payoff here is especially strong for developers: your assistant can now search your internal knowledge base — architecture docs, runbooks, API references, past decisions — from inside the editor, while you're writing code. Ask "how does our auth flow work?" and the assistant retrieves the answer from your actual documentation instead of guessing or hallucinating. The knowledge base becomes codebase-adjacent context, available where the work happens.
What "it just works" actually buys you
Because all three clients speak MCP, a few things fall out for free:
- No per-client integration. You didn't write a Claude plugin, a Cursor extension, and a Windsurf plugin. You exposed one MCP server and connected three clients to it. That's the M+N collapse MCP is built for.
- Consistent behavior. The same tools, the same retrieval quality, the same citations, everywhere. Improve the server (better ranking, fresher content) and every client benefits at once.
- Future clients for free. The next MCP-compatible tool your team adopts connects to the same server with no new work.
Tips for a good experience
- Write clear tool descriptions. The client shows the model your tool's description to decide when to call it. "Search the company knowledge base for product, policy, and documentation questions — use before answering such questions" guides the model far better than a terse name. This is the single biggest lever on whether the model actually uses your tool at the right moments.
- Return sources with results. So the client can cite them and the user can verify. Provenance is what makes the grounded answers trustworthy.
- Scope credentials to least privilege. Give each client read/search access, not more. If a client only needs to search, it shouldn't be able to modify.
- Use proper authorization for anything shared. A personal local setup can be simple; a server your whole team's tools connect to deserves scoped, revocable auth.
The takeaway
Connecting your knowledge base to Claude, Cursor, and Windsurf isn't three integration projects — it's one MCP server and three quick client configurations. That's the entire value of the standard: you make your knowledge available once, and every AI tool your team uses can reach it. The result is that your documentation, decisions, and internal knowledge stop living in a portal people forget to check and start showing up inside the tools where work happens — answering questions in Claude, grounding code in Cursor and Windsurf, and ready for whatever MCP client comes next. Build once, connect everywhere, exactly as advertised.