Model Context Protocol (MCP): A New Standard for LLM Interaction

Model Context Protocol (MCP) is an open standard designed to provide secure and efficient interaction between Large Language Models (LLMs) and external tools. In this article, we’ll explore what MCP is, how it works, what benefits it offers to developers, and why it’s important for the development of the artificial intelligence ecosystem and vibe coding.

What is Model Context Protocol?

Model Context Protocol (MCP) is an open protocol developed by Anthropic in collaboration with Microsoft and other technology companies. It is designed to standardize the way language models interact with external tools and services, providing secure and controlled access to various functions and data.

MCP addresses a key problem of modern LLMs: how to provide models with access to external tools and data while maintaining security and control. The protocol defines a standardized way to describe tools, their capabilities and limitations, as well as the format for data exchange between LLMs and these tools.

The main goal of MCP is to create a unified standard that will allow developers to create tools compatible with various language models, and users to safely use these tools in their applications.

Architecture and Components of MCP

Model Context Protocol is built on a client-server architecture, where the language model acts as a client and external tools act as servers. Let’s look at the main components of this architecture:

Client (LLM)

The client part of MCP is integrated into the language model and is responsible for:

• Forming requests to MCP servers
• Processing responses from servers
• Integrating the received information into the model’s context

MCP Server

The server part implements access to specific tools or services and includes:

• Description of available tools and their capabilities
• Processing requests from the client
• Executing requested operations
• Returning results to the client

Transport Layer

MCP defines a standard format for data exchange between client and server, which can be implemented through various transport protocols:

• Standard input/output (stdio)
• HTTP/HTTPS
• WebSockets
• Other data transfer protocols

Connection Lifecycle

The interaction between client and server in MCP follows the following scenario:

sequenceDiagram
    participant LLM as Language Model (Client)
    participant MCP as MCP Server
    participant Tool as External Tool

    LLM->>MCP: Request list of available tools
    MCP->>LLM: List of tools with descriptions and input schemas
    LLM->>MCP: Request to use a tool
    MCP->>Tool: Execute operation
    Tool->>MCP: Operation result
    MCP->>LLM: Result in standardized format
    LLM->>LLM: Integrate result into context

Key Concepts of MCP

Tools

Tools are the basic functional units in MCP. Each tool has:

• A unique name
• Description of functionality
• Input data schema (inputSchema)
• Output data format

Tools can perform various functions: from searching for information on the internet to managing the file system or interacting with databases.

Resources

Resources represent data that LLM can access through MCP. They may include:

• Files and documents
• Databases
• APIs and web services
• Other information sources

Roots

Roots in MCP define entry points for accessing resources. They provide a structured and secure way to navigate through available resources.

Data Schemas

MCP uses JSON Schema to describe the structure of input and output data for tools, which provides:

• Strict data typing
• Validation of input parameters
• Documentation of the expected data format

Comparing MCP with Other Protocols

To better understand MCP’s place in the AI ecosystem, let’s compare it with other similar solutions:

graph TD
    subgraph "LLM Interaction Protocols"
        MCP["Model Context Protocol (MCP)"]
        OpenAI["OpenAI Function Calling"]
        LCEL["LangChain Expression Language"]
        AGI["AI Agent Protocols"]
    end
    
    subgraph "Features"
        OS["Open Standard"]
        SEC["Security"]
        COMP["Compatibility"]
        FLEX["Flexibility"]
        STD["Standardization"]
    end
    
    MCP --> OS
    MCP --> SEC
    MCP --> COMP
    MCP --> FLEX
    MCP --> STD
    
    OpenAI --> SEC
    OpenAI --> FLEX
    
    LCEL --> FLEX
    LCEL --> COMP
    
    AGI --> OS
    AGI --> COMP

MCP vs OpenAI Function Calling

MCP vs LangChain

Examples of MCP Usage

Example 1: File System

One of the basic examples of using MCP is access to the file system. Here’s what a server implementation for working with files looks like:

// Server initialization
const server = new Server(
  {
    name: "secure-filesystem-server",
    version: "0.2.0",
  },
  {
    capabilities: {
      tools: {},
    },
  },
);

// Defining a tool for reading a file
server.setRequestHandler(ListToolsRequestSchema, async () => {
  return {
    tools: [
      {
        name: "read_file",
        description: "Read complete contents of a file",
        inputSchema: {
          type: "object",
          properties: {
            path: {
              type: "string",
              description: "Path to the file to read",
            },
          },
          required: ["path"],
        },
      },
      // Other tools...
    ],
  };
});

// Handler for reading a file
server.setRequestHandler(CallToolRequestSchema, async (request) => {
  if (request.tool === "read_file") {
    const { path: filePath } = request.input as { path: string };
    try {
      const validatedPath = await validatePath(filePath);
      const content = await fs.readFile(validatedPath, "utf-8");
      return { result: content };
    } catch (error) {
      return { error: `Error reading file: ${error.message}` };
    }
  }
  // Handlers for other tools...
});

Example 2: Knowledge Graph (Memory)

Another interesting example is a server for working with a knowledge graph that allows LLM to store and retrieve information between sessions:

// Knowledge graph structure
interface Entity {
  name: string;
  entityType: string;
  observations: string[];
}

interface Relation {
  from: string;
  to: string;
  relationType: string;
}

// Tool for creating entities
{
  name: "create_entities",
  description: "Create multiple new entities in the knowledge graph",
  inputSchema: {
    type: "object",
    properties: {
      entities: {
        type: "array",
        items: {
          type: "object",
          properties: {
            name: { type: "string", description: "The name of the entity" },
            entityType: { type: "string", description: "The type of the entity" },
            observations: {
              type: "array",
              items: { type: "string" },
              description: "An array of observation contents associated with the entity"
            },
          },
          required: ["name", "entityType", "observations"],
        },
      },
    },
    required: ["entities"],
  },
}

Benefits of MCP for Developers

Model Context Protocol provides developers with a number of significant advantages:

1. Standardization

MCP offers a unified standard for interacting with various language models, which allows:

• Creating tools compatible with different LLMs
• Reducing ecosystem fragmentation
• Simplifying integration of new models and tools

2. Security

The protocol includes built-in security mechanisms:

• Strict validation of input and output data
• Access control to resources
• Isolation of operation execution
• Prevention of potentially dangerous actions

3. Flexibility and Extensibility

MCP is designed with extensibility in mind:

• Support for various types of tools
• Ability to add new features
• Adaptation to different use cases
• Support for various transport protocols

4. Openness

As an open standard, MCP provides:

• Implementation transparency
• Opportunity for community participation in development
• No vendor lock-in
• Long-term sustainability of solutions

5. Compatibility

MCP ensures compatibility between:

• Various language models
• Various platforms and runtime environments
• Various programming languages
• Existing and future tools

Value of MCP for Vibe Coding

The concept of Vibe Coding implies creating a more natural, intuitive, and productive software development process. Model Context Protocol makes a significant contribution to this concept:

1. Improving Human-AI Interaction

MCP creates a more natural interface between developers and AI:

• Allows models to use tools in the same way humans do
• Provides predictable and understandable AI behavior
• Simplifies delegating tasks to AI assistants

2. Expanding AI Assistant Capabilities

With MCP, AI assistants can:

• Access up-to-date information
• Use specialized tools
• Perform complex sequences of actions
• Adapt to specific developer needs

3. Creating a Tool Ecosystem

MCP contributes to the development of a tool ecosystem for Vibe Coding:

• Standardized way to create tools
• Ability to share and reuse tools
• Lowering the barrier to creating new tools
• Integration with existing development tools

4. Increasing Productivity

Using MCP in the context of Vibe Coding leads to:

• Reducing time spent on routine tasks
• More efficient search and use of information
• Automation of complex workflows
• Focusing developers on creative aspects of work

5. Democratization of AI Tools

MCP contributes to democratizing access to AI tools:

• Lowering the technical barrier to creating AI applications
• Enabling small teams to create powerful tools
• Expanding access to advanced AI capabilities
• Creating a more inclusive development environment

Practical Application of MCP

Integration with Existing Projects

To integrate MCP with existing projects, you can use official SDKs:

• Python SDK: pip install modelcontextprotocol
• TypeScript SDK: npm install @modelcontextprotocol/sdk

Example of integration with a Python project:

from modelcontextprotocol.server import Server
from modelcontextprotocol.server.stdio import StdioServerTransport
from modelcontextprotocol.types import CallToolRequestSchema, ListToolsRequestSchema

# Creating a server
server = Server(
    metadata={"name": "example-server", "version": "1.0.0"},
    capabilities={"tools": {}}
)

# Registering handlers
@server.request_handler(ListToolsRequestSchema)
async def handle_list_tools():
    return {"tools": [...]}

@server.request_handler(CallToolRequestSchema)
async def handle_call_tool(request):
    # Processing tool call
    pass

# Starting the server
transport = StdioServerTransport()
server.listen(transport)

Creating New Tools

Creating new tools for MCP includes the following steps:

1. Defining tool functionality
2. Creating input data schema
3. Implementing request processing logic
4. Testing the tool with various LLMs

Deploying MCP Servers

MCP servers can be deployed in various ways:

• Locally for personal use
• In Docker containers for isolation and scaling
• In cloud infrastructure for shared access
• As part of larger applications

The Future of MCP

Model Context Protocol is in an active development phase, and its future looks promising:

Ecosystem Expansion

• Increasing the number of supported language models
• Growing number of available tools
• Integration with popular platforms and frameworks
• Creation of tool marketplaces

Standard Development

• Adding new features and capabilities
• Improving security and performance
• Expanding the range of supported use cases
• Integration with other standards and protocols

Industry Impact

• Stimulating standardization in the AI field
• Accelerating the development of AI tools
• Increasing accessibility of AI technologies
• Forming new approaches to software development

Conclusion

Model Context Protocol represents an important step in the development of the artificial intelligence ecosystem and Vibe Coding. It addresses the key problem of interaction between language models and external tools, offering a standardized, secure, and flexible approach.

For developers, MCP opens new possibilities for creating innovative tools and applications that leverage the potential of modern language models. For users, it provides a more natural and productive interaction with AI assistants.

In the context of Vibe Coding, MCP contributes to creating a more intuitive, efficient, and creative development process, where AI acts as a true assistant, expanding human capabilities.

As the MCP ecosystem develops and the number of supported tools and models increases, its influence on the industry will only grow, shaping the future of human-artificial intelligence interaction.

References and Resources

• Official Model Context Protocol Website
• MCP GitHub Repository
• Anthropic Documentation on MCP
• Microsoft Documentation on MCP Integration in Copilot Studio