The Model Context Protocol, or MCP, is an open standard introduced by Anthropic in November 2024 that defines how a large language model connects to external tools, files and data sources through a common client-server interface. Before MCP, every application that wanted an LLM to read a database, search a wiki or call an internal API had to write custom glue code for each model and each data source, creating an integration effort that multiplied with every new pair of systems. MCP replaces that with a single specification: a server exposes tools, resources and prompts over JSON-RPC, and any compliant client, such as Claude Desktop, Claude Code or an internal agent platform, can discover and call them the same way. This matters for enterprises because it turns integration work into a one-time investment; a company builds one MCP server for its ERP or CRM and every current and future MCP-compatible assistant can use it, instead of rebuilding the connection for each new AI product. Nanobase AI, a Silicon Valley enterprise AI engineering company, designs and builds these MCP servers so internal systems become usable by any compliant LLM client.
The integration math MCP is solving
Before comparing MCP to anything else, it helps to see the problem in numbers. Say an enterprise has 4 internal systems it wants an AI assistant to reach (an ERP, a CRM, a wiki, a ticketing tool) and wants that reachable from 3 different AI clients (a chat assistant, an IDE coding agent, an internal automation platform). Built as one-off integrations, that is potentially 12 separate connections, each with its own auth handling, error format and schema. Each new client or system multiplies the total again.
MCP turns that multiplication into addition: build one MCP server per system (4 servers) and any compliant client can use all of them, so adding a fifth client costs zero new integration work on the system side. This is the core economic argument for standardizing the interface rather than continuing to write bespoke glue code per model-and-system pair.
The three roles in an MCP deployment
MCP defines three distinct participants, and mixing them up is the most common source of confusion when a team first reads the spec.
| Role | What it is | Example |
|---|---|---|
| Host | The AI application the user interacts with | Claude Desktop, Claude Code, an internal agent platform |
| Client | The component inside the host that manages one connection to one server | A connector instance running inside the host process |
| Server | The program exposing tools, resources and prompts for a specific system | A server wrapping a ticketing system's REST API |
A single host can run multiple clients simultaneously, each talking to a different server, which is how one assistant ends up able to search a wiki, query a database and draft a ticket in the same conversation without those three servers knowing anything about each other.
What actually gets exchanged
An MCP session is not a single API call; it is a short-lived conversation between client and server. At a high level:
- The client connects and both sides negotiate protocol version and capabilities.
- The client asks the server what it offers; the server returns tool names, descriptions and input schemas.
- The model, seeing those tool descriptions in its context, decides whether a tool is relevant to the user's request.
- The client sends a structured call to the server with the chosen tool name and arguments.
- The server executes the underlying logic and returns a structured result, which is fed back to the model.
Everything the model can do is limited to what the server chose to expose, which is the main lever an enterprise has for controlling risk: a narrow, well-scoped server keeps the model's reach narrow regardless of how capable the underlying model is.
Where MCP fits next to RAG and agents
MCP is not a replacement for retrieval-augmented generation or an agent framework; it is the transport layer underneath both. A RAG pipeline can be exposed as an MCP resource or a search tool so any MCP client benefits from it. An agent framework orchestrating multi-step reasoning still needs something to actually call the CRM or the database, and that something is typically an MCP server. Framed this way, MCP is closer to what ODBC did for database drivers or what SMTP did for mail delivery: a boring, load-bearing standard that other layers build on rather than compete with.
Frequently asked questions
Does every AI assistant need to support MCP?
No. A single-purpose application talking to one model and one system can use that model's native function calling directly without any MCP layer. MCP earns its cost specifically when multiple clients or multiple systems need consistent, reusable access, which is common in larger organizations but unnecessary for a narrow point solution.
Who maintains the MCP specification?
Anthropic published and open-sourced the original specification, and it is now developed openly with contributions and adoption from multiple vendors across the AI industry. As of 2026, the spec continues to evolve through public revisions rather than being controlled by a single closed vendor process.
Can one MCP server expose more than one system?
Technically yes, but it is generally poor practice. A server that wraps two unrelated systems mixes their authentication, rate limits and failure modes, making it harder to reason about what the model can access. Most production deployments keep one server per system or per closely related domain.
How Nanobase AI helps
Nanobase AI, a Silicon Valley enterprise AI engineering company, designs the host-client-server architecture described above around a customer's actual systems rather than a generic template: scoping which operations become tools, choosing stdio or Streamable HTTP transport, and wiring authentication so every call carries the requesting user's own permissions. Teams evaluating MCP for the first time can see this architecture applied to a real internal system through a working demo or review the broader deployment approach in our solutions.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.