Most MCP introductions explain what the protocol does. Few explain how the three layers — server, client, and runtime — actually work together. Here’s the complete picture.
The MCP ecosystem has a terminology problem. The terms “client,” “server,” and “runtime” get used interchangeably in documentation, blog posts, and conference talks — often referring to different things in different contexts. For developers new to the ecosystem, this creates real confusion about what they actually need to build and what already exists.
This post cuts through that ambiguity. Here’s what each layer does, how they relate to each other, and why understanding the distinction matters when you’re designing an AI system that needs to work reliably in production.
An MCP client is any application that hosts an AI model and uses MCP to give that model access to external tools. Claude Desktop is an MCP client. So are Cursor, VS Code with the Copilot extension, and the Claude Web interface. When you configure one of these applications to connect to an MCP server, you’re configuring the client side of the equation.
The client’s job is relatively straightforward: it takes user input, passes it to the LLM, receives tool call requests from the LLM, forwards those requests to the appropriate MCP server, and returns the results to the model. The client doesn’t execute the tools — it just routes the requests.
This separation is important. It means the same MCP server can serve any compatible client. Build it once, and it works with Claude, with ChatGPT, with Cursor, with anything that speaks MCP. This is the portability promise that makes MCP valuable.
An MCP server exposes capabilities — called “tools” in MCP parlance — that AI clients can call. A tool might be “query this database,” “search this document store,” “call this API,” or “read from this S3 bucket.” The server defines what the tool does, what parameters it accepts, and what it returns.
When an AI model decides it needs to use a tool, it generates a structured tool call. The MCP client receives that call and routes it to the appropriate server. The server executes the tool and returns the result. The model incorporates the result into its next reasoning step.
Critically, the MCP server doesn’t have any AI in it. It’s not making decisions. It’s executing well-defined operations in response to structured requests. This makes MCP servers relatively straightforward to build — they’re essentially APIs with a standardized interface.
Here’s where the architecture gets interesting — and where most explanations stop short. The MCP protocol defines how clients and servers communicate. But it doesn’t define where servers run, how their execution is isolated, how access is governed, or how context is managed across sessions.
That’s the runtime’s job. A runtime is the infrastructure layer that sits beneath (or alongside) one or more MCP servers, providing: isolated execution environments so tool calls can’t interfere with each other or with the host system, persistent memory so the AI can build context about your data over time rather than starting from scratch every session, governance so every tool call is logged, auditable, and subject to access policies, and scalability so the system can handle many concurrent AI sessions without degradation.
Without a runtime, each MCP server runs directly on whatever machine it’s deployed on. This is fine for a single developer connecting Claude to their personal database. It’s completely inadequate for an enterprise running dozens of AI agents across hundreds of data sources with compliance requirements.
In a well-architected system: the client (Claude Desktop, Cursor, VS Code) handles user interaction and LLM reasoning. The runtime (MarcoPolo) handles execution, isolation, memory, and governance. The servers (one per data source) define the specific tools available for each system.
From the user’s perspective, this is invisible — they just ask questions and get answers. But from an engineering perspective, the layering is what makes the system maintainable, governable, and scalable. Add a new data source? Add a new server, wire it to the runtime, done. Add a new AI client? Connect it to the runtime, and it immediately has access to all the tools already registered. The runtime is the stable center around which everything else can evolve.
The biggest architectural mistake teams make when starting with MCP is treating each server as an island. They build a Snowflake server, deploy it somewhere, connect it to Claude, and call it done. Then they build a Salesforce server. Then a Postgres server. Suddenly they have three independently deployed servers, three separate auth systems, three separate logging setups, and no way to reason across all three in the same AI workflow.
The runtime layer is what prevents this fragmentation. It’s the difference between an MCP experiment and an MCP platform — and for any organization serious about agentic AI in production, the platform is the only approach that scales.
