Before USB-C, every device had its own charger and cable. A drawer full of proprietary connectors, none interchangeable, each solving the same problem differently. The Model Context Protocol is USB-C for AI: one standard way to plug a model into tools and data, replacing a drawer full of custom integrations.
That analogy gets thrown around a lot. It's worth understanding why it's apt, because it explains why MCP went from a single vendor's spec to an industry default remarkably fast.
The problem MCP solves
Say you have M AI applications (Claude, an IDE assistant, an internal agent) and N tools you want them to reach (your knowledge base, a ticketing system, a database). Without a standard, connecting them is an M×N problem: every app needs a bespoke integration for every tool. Add one new tool and you're writing M new integrations. Add one new app and it's N. The glue code multiplies faster than anyone can maintain it.
MCP collapses M×N into M+N. Each tool is exposed once as an MCP server. Each app implements the MCP client protocol once. Now any client can talk to any server through the same interface. Build your knowledge base as an MCP server one time, and Claude, Cursor, Windsurf, and your own agent can all use it without you writing a line of per-client integration code. That collapse from multiplication to addition is the whole value proposition, and it's why adoption snowballed — every new client makes every existing server more useful, and vice versa.
What MCP actually is
MCP is a protocol — a shared contract for how a model-facing client and a tool-providing server talk. A server exposes a few kinds of things:
- Tools — actions the model can invoke:
search_knowledge_base,create_ticket,run_query. The model decides when to call them and with what arguments. - Resources — data the client can read: files, records, documents.
- Prompts — reusable prompt templates the server offers to clients.
The client (the AI app) discovers what a server offers, presents those capabilities to the model, and relays calls back and forth. Crucially, the model doesn't need to be trained on your specific tool — it reads the tool's description at runtime and figures out how to use it. That's what makes MCP composable: you can add a new capability to a running system without retraining or redeploying the model.
AI app (MCP client) MCP server (your tool)
│ "what can you do?" │
│ ──────────────────────────────────▶│
│ tools: [search_kb, get_doc] │
│ ◀──────────────────────────────────│
│ call search_kb("VPN setup") │
│ ──────────────────────────────────▶│
│ results: [...] │
│ ◀──────────────────────────────────│
Why it matters for knowledge
Knowledge retrieval turned out to be one of MCP's killer applications. An AI assistant is only as good as what it can reach, and the single most valuable thing to hand it is your knowledge — your docs, your policies, your product details. MCP is the clean way to do that.
Expose your knowledge base as an MCP server with a search_knowledge_base tool, and any MCP-compatible assistant can ground its answers in your content. The model decides when it needs to look something up, calls the tool, reads the results, and answers with sources. No custom integration per assistant. No copying your docs into each tool's context. One server, every client.
This is why Kognita ships an MCP server for every knowledge base out of the box. You ingest your content once; it's immediately reachable as a standard tool from Claude, from coding assistants, from any agent you build. The knowledge base stops being a destination users visit and becomes a capability their AI tools already have.
Where it's going
MCP is still young and moving fast. The direction of travel is toward richer patterns: enterprise-grade authentication (OAuth 2.1 and managed authorization for secure deployments), asynchronous tools for long-running jobs that don't fit a single request/response, and a discovery registry — an npm-like index for finding and publishing servers. The through-line is the same standardization instinct that made the core protocol take off: once the plug is standard, the ecosystem builds outward from it.
The takeaway
MCP isn't a model, a framework, or a product. It's a standard — the boring, load-bearing kind of infrastructure that wins not by being clever but by being agreed upon. Its value is entirely in the network: every client that speaks it makes every server more useful.
If you're building anything an AI assistant should be able to use — and a knowledge base is the canonical example — exposing it over MCP means you build the integration once and reach every current and future client for free. That's the USB-C promise. One plug, everything connects.