MCP Server vs Client vs Host: Complete Architecture Guide
MCPNerds head writer
If you're building AI agents or working with modern AI systems, you've probably heard about Model Context Protocol (MCP). But the terminology can be confusing. What exactly is an MCP server? How does it differ from a client or host? And why should you care?
Let me break down these core concepts in plain English, because understanding this architecture is crucial for anyone building AI applications that need to interact with external tools and data.
What is Model Context Protocol (MCP)?
Before diving into the components, let's establish what MCP actually is. The Model Context Protocol is an open standard that enables developers to build secure, two-way connections between their data sources and AI-powered tools. Model Context Protocol was created by Anthropic to standardize how AI models connect to external tools and data sources.
Think of it as the USB-C for AI applications. Just like USB-C eliminated the need for dozens of different cables, MCP eliminates the need for custom integrations between every AI model and every external tool.
The protocol is already being adopted by major platforms like Block and Apollo, while development tools companies including Zed, Replit, Codeium, and Sourcegraph are working with MCP to enhance their platforms. This tells you everything you need to know about its practical value.
The Three Components: Host, Client, and Server
MCP operates through three distinct components: MCP Client (a component that maintains a connection to an MCP server and obtains context from an MCP server for the MCP host to use), MCP Server (a program that provides context to MCP clients), and the MCP host.
The MCP Host
The host application is where LLMs interact with users and initiate connections. This includes Claude Desktop, AI-enhanced IDEs like Cursor, and standard web-based LLM chat interfaces. The host is the "brain" that decides what needs to be done and initiates requests.
Think of the host as a tourist in a foreign country who needs to order food but doesn't speak the local language.
The MCP Client
The client is a component that maintains a connection to an MCP server and obtains context from an MCP server for the MCP host to use. It sits between the host and external services, handling all the communication protocols and message formatting. The client knows how to talk to both the AI model and the external tools.
In our analogy, the client is the helpful translator who speaks both languages and facilitates the conversation.
The MCP Server
The server is a program that provides context to MCP clients. This could be a calendar system, a database, a code execution environment, or any other external resource that the AI needs to access.
The server is like the restaurant server who only speaks the local language and can fulfill requests but needs the translator to understand what's being asked.
How They Work Together: A Real Example
Let's walk through a concrete example. Say you ask your AI assistant: "Schedule a meeting with Sarah for tomorrow at 2 PM."
Step 1: The Host Initiates Your AI assistant (the host) realizes it needs to create a calendar event. It doesn't know how to talk to Google Calendar directly, so it tells its MCP client to use the createEvent tool with the meeting details.
Step 2: The Client Translates The MCP client takes this request and formats it properly using JSON-RPC 2.0, which is a remote procedure call (RPC) standard that supports structured requests, named parameters, and versioned responses. It sends this formatted request to the Google Calendar MCP server and waits for a response.
Step 3: The Server Acts The MCP server receives the request, makes the actual API call to Google Calendar to create the event, and sends back the result (success, error, or confirmation details).
Step 4: Response Flows Back The server's response goes back through the client, which translates it into a format the host can understand, and finally you see whether your meeting was successfully scheduled.
Common Misconceptions About MCP Servers
Here's where things get confusing for many developers: despite the name "MCP server," you're not necessarily dealing with a traditional web server running in the cloud.
MCP servers can be run locally or remotely. Local MCP servers are those that run in a host that we control and usually execute OS commands or custom code locally.
However, this is evolving. Companies like Dedalus Labs are building cloud-hosted MCP infrastructure that handles the server management for you. Anthropic maintains an open-source repository of reference MCP server implementations for popular enterprise systems including Google Drive, Slack, GitHub, Git, Postgres, Puppeteer and Stripe.
But for most developers starting out, you can run MCP servers right on your laptop without any cloud deployment complexity.
Why This Architecture Matters
The beauty of MCP's three-component architecture is in its flexibility and security:
Security: All MCP client-server interactions are routed through a trusted proxy, enabling centralized enforcement of policies and consent. This includes the ability to enforce authentication and authorization in a centralized and consistent manner addressing one of the top challenges with the MCP protocol.
Scalability: A single MCP client can have access to one or more MCP servers, and users may connect any number of MCP servers to an MCP client. Your AI agent could be connected to GitHub, Jira, Slack, and your internal documentation all at once.
Simplicity: Instead of maintaining separate connectors for each data source, developers can now build against a standard protocol.
Model Agnostic: MCP brings similar capabilities to any AI application that implements the protocol, regardless of the underlying model vendor.
Security Considerations
Understanding MCP architecture is crucial for security. MCP servers pose significant security risks due to their ability to execute commands and perform API calls. One major concern is that even if a user doesn't intend a specific action, the LLM might decide it's the appropriate one. This risk can arise without malicious intent, but it's also a factor in adversarial scenarios.
In the case of a simple chat app, the implications of a prompt injection could be a jailbreak or leakage of memory data, with MCP the implications could be full remote code execution — the highest severity attack.
That's why Dedalus Labs has built the most secure MCP infrastructure available, with enterprise-grade security controls and monitoring built in from day one.
Getting Started with MCP
If you're ready to start building with MCP, here's what I recommend:
- Start Local: Begin with simple, locally-hosted MCP servers to understand the concepts
- Use Existing Servers: Check out pre-built MCP servers for popular enterprise systems like Google Drive, Slack, GitHub, Git, Postgres, and Puppeteer
- Consider Managed Solutions: For production applications, platforms like Dedalus Labs can handle the infrastructure complexity and provide the highest level of security
The MCP ecosystem is growing rapidly, and understanding these core concepts now will put you ahead of the curve as AI agents become more prevalent in software development.
Conclusion
MCP's host-client-server architecture might seem complex at first, but it's actually elegant in its simplicity. As the ecosystem matures, AI systems will maintain context as they move between different tools and datasets, replacing today's fragmented integrations with a more sustainable architecture.
Whether you're building AI assistants, coding copilots, or workflow automation tools, MCP provides the foundation for reliable, scalable connections to external services. Instead of wrestling with custom API integrations, you can focus on building the features that matter to your users.
The protocol's rapid uptake by OpenAI, Google DeepMind, and toolmakers like Zed and Sourcegraph suggests growing consensus around its utility. Understanding these architectural concepts now will serve you well as you build the next generation of AI applications.
FAQ
What's the difference between an MCP server and a regular web server?
Despite the name, MCP servers are often lightweight local applications rather than traditional web servers. They use JSON-RPC 2.0 for communication and focus on providing specific tools or data to AI systems.
Can one host connect to multiple MCP servers?
Yes, one MCP client has access to one or more MCP servers. Users may connect any number of MCP servers to an MCP client. This allows AI agents to access many different tools and data sources simultaneously.
Do I need to host my own MCP servers?
Not necessarily. You can run servers locally for development, use hosted solutions from companies like Dedalus Labs, or connect to servers provided by other organizations.
Is MCP only for Anthropic's Claude?
No, MCP brings similar capabilities to any AI application that implements the protocol, regardless of the underlying model vendor. While Anthropic created it, it's designed to be model-agnostic.
How secure is MCP?
MCP is designed with security in mind. All client-server interactions can be routed through a trusted proxy, enabling centralized enforcement of policies and consent, including the ability to enforce authentication and authorization in a centralized manner. However, proper implementation and security controls are essential.
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