Exploration of the Model Context Protocol (MCP)

Introduction

The Model Context Protocol (MCP), introduced by Anthropic, represents a significant advancement in the integration of artificial intelligence, particularly Large Language Models (LLMs), with external data sources and tools. As of March 21, 2025, MCP is gaining traction for its potential to enhance AI capabilities by providing a standardized interface, akin to a USB-C port for AI applications. This survey note aims to provide a thorough examination of MCP, covering its definition, usage, and construction, drawing from recent documentation and community insights.

Background and Definition

MCP is an open standard designed to facilitate seamless integration between LLM applications and external data sources, such as content repositories, business tools, and development environments. It addresses the challenge of fragmented data connections, where traditional AI systems require custom integrations for each new data source, leading to inefficiencies. MCP, as described in Introducing the Model Context Protocol , aims to help frontier models produce better, more relevant responses by connecting them to the systems where data resides.

The protocol is likened to a USB-C port, offering a universal method for AI models to interact with various tools and data sources, as noted in Introduction - Model Context Protocol . This standardization is crucial for building agents and complex workflows, providing pre-built integrations, flexibility across LLM providers, and security best practices.

Architectural Overview

MCP operates on a client-server architecture, detailed in Core architecture - Model Context Protocol . The architecture includes:

MCP Hosts: Applications like Claude Desktop, IDEs, or AI tools that initiate connections to access data.
MCP Clients: Components within the host that maintain 1:1 connections with servers, facilitating communication.
MCP Servers: Lightweight programs that expose capabilities, such as tools, prompts, and resources, to the clients.

The protocol layer handles message framing, request/response linking, and communication patterns, supporting transports like Stdio for local processes and HTTP with Server-Sent Events (SSE) for remote connections. Message types include requests, results, errors, and notifications, with a defined lifecycle involving initialization, message exchange, and termination.

Aspect	Details
Architecture Type	Client-server, with hosts initiating, clients connecting, servers providing context.
Core Components	Protocol layer (message handling), Transport layer (Stdio, HTTP/SSE), using JSON-RPC 2.0.
Message Types	Requests (expect response), Results (success), Errors (failure codes), Notifications (one-way).
Connection Lifecycle	Initialization, exchange, termination, with error handling and security considerations.
Transports	Stdio (local), HTTP/SSE (remote), with best practices for selection and security.

This architecture ensures flexibility and scalability, as highlighted in What is Model Context Protocol? , which notes MCP’s role in managing context efficiently across LLM use cases.

Usage of MCP

Using MCP involves setting up an MCP-compatible host, such as the Claude desktop app, and configuring it to connect to MCP servers. A practical example, as outlined in Getting Started: Model Context Protocol , is adding a Knowledge Graph Memory Server:

Download the Claude desktop app from this website .

Edit the claude_desktop_config.json file, adding a configuration like:

json


{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-memory"]
    }
  }
}

Save and restart the app, then use prompts to interact, such as creating knowledge graphs from documents.

MCP also offers pre-built servers for systems like Google Drive, Slack, and GitHub, listed at GitHub servers , enhancing its usability for developers. This accessibility is particularly useful for integrating AI with enterprise tools, as noted in Introducing the Model Context Protocol , where early adopters like Block and Apollo have integrated MCP.

Building an MCP Server

Building an MCP server allows developers to expose custom tools and data sources. The Quickstart – Model Context Protocol provides a detailed guide for constructing a simple weather server, which can be implemented in Python or TypeScript. The process includes:

Step	Details
Prerequisites	Python 3.9+ or Node.js 16+, familiarity with LLMs like Claude.
Setup (Python)	Install uv, initialize project, install mcp httpx, create server files.
Setup (TypeScript)	Install Node.js, initialize with npm, install @modelcontextprotocol/sdk zod @types/node typescript.
Implementation	Define server, implement list_tools and call_tool methods, handle API calls (e.g., NWS API).
Testing	Configure Claude desktop, test with commands like “What’s the weather in Sacramento?”.

For Python, the server exposes tools like get-alerts and get-forecast, using the National Weather Service API. The implementation involves setting up the environment, coding the server logic, and testing with Claude Desktop, ensuring compatibility and functionality.

Implications and Future Directions

MCP addresses the fragmentation of AI integrations, transforming the M × N problem (connecting M clients with N resources) into an M + N problem through standardization, as discussed in Model Context Protocol (MCP) - NSHipster . This comparison to the Language Server Protocol (LSP) highlights MCP’s potential to revolutionize AI tool integration, though its ecosystem is still maturing, with some user experiences noted as rough in Teaching your AI to do stuff — Model Context Protocol .

The open-source nature of MCP, managed by Anthropic and open to community contributions, as seen at Model Context Protocol · GitHub , encourages developer participation. Future growth may include more pre-built servers, enhanced security features, and broader adoption across industries, potentially transforming how AI interacts with data.

Conclusion

The Model Context Protocol (MCP) is a pivotal development for AI integration, offering a standardized, flexible, and secure way to connect LLMs with data sources and tools. Its usage is accessible through applications like Claude Desktop, and building custom servers empowers developers to tailor solutions. As of March 21, 2025, MCP’s ecosystem is expanding, promising to enhance AI capabilities and foster innovation in the field.

Research suggests MCP, or Model Context Protocol, is an open standard by Anthropic for connecting AI, especially Large Language Models (LLMs), to data sources and tools, enhancing their functionality.
It seems likely that MCP simplifies AI integrations, offering pre-built connections for systems like Google Drive and GitHub, making it easier to use in applications.
The evidence leans toward MCP following a client-server architecture, with hosts like Claude Desktop and servers exposing tools, which users can build and customize for specific needs.

Key Citations

FaQs

What is MCP?

MCP, or the Model Context Protocol, is designed to bridge AI models with external data and tools. Think of it like a universal adapter, similar to a USB-C port, but for AI. It helps LLMs access real-time information, making them more useful for tasks like answering queries with up-to-date data or automating workflows.

How to Use MCP?

To use MCP, you can start with the Claude desktop app, available at this website . Configure it by adding servers, like a Knowledge Graph Memory Server, by editing a settings file. This lets you ask the AI to, for example, create knowledge graphs from papers. MCP also offers pre-built servers for popular tools, which you can explore at GitHub servers .

How to Build One?

Building an MCP server involves creating a program that exposes tools, like a weather server that fetches alerts. You can use Python or TypeScript, following guides like this quickstart , which walks you through setting up, coding, and testing with Claude Desktop. It’s a hands-on way to tailor MCP for your needs, such as connecting to a specific API.