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MCPIntroductionOverview

What is the Model Context Protocol (MCP)?

MCP is an open-source standard for connecting AI applications to external systems. Think of MCP like a USB-C port for AI applications - providing a standardized way to connect AI to data sources, tools, and workflows.

January 15, 2025
8 min read
21nauts Team

What is the Model Context Protocol (MCP)?

MCP (Model Context Protocol) is an open-source standard for connecting AI applications to external systems. Using MCP, AI applications like Claude or ChatGPT can connect to data sources (e.g. local files, databases), tools (e.g. search engines, calculators) and workflows (e.g. specialized prompts)—enabling them to access key information and perform tasks.

Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect electronic devices, MCP provides a standardized way to connect AI applications to external systems.

What can MCP enable?

MCP opens up incredible possibilities for AI applications:

  • Agents can access your Google Calendar and Notion, acting as a more personalized AI assistant
  • Claude Code can generate an entire web app using a Figma design
  • Enterprise chatbots can connect to multiple databases across an organization, empowering users to analyze data using chat
  • AI models can create 3D designs on Blender and print them out using a 3D printer

Why does MCP matter?

Depending on where you sit in the ecosystem, MCP can have a range of benefits:

For Developers

MCP reduces development time and complexity when building, or integrating with, an AI application or agent.

For AI Applications or Agents

MCP provides access to an ecosystem of data sources, tools and apps which will enhance capabilities and improve the end-user experience.

For End-Users

MCP results in more capable AI applications or agents which can access your data and take actions on your behalf when necessary.

The Problem MCP Solves

Before MCP, building AI systems often involved:

  • Custom implementations for each AI application to hook into its required context, leading to duplicated effort
  • Inconsistent prompt logic and different methods for accessing and federating tools and data across teams
  • The "N times M problem" where many client applications needed to interact with many servers and tools, resulting in complex integrations

How MCP Works

MCP follows a client-host-server architecture where each host can run multiple client instances:

Host

The host process acts as the container and coordinator:

  • Creates and manages multiple client instances
  • Controls client connection permissions and lifecycle
  • Enforces security policies and consent requirements
  • Handles user authorization decisions
  • Coordinates AI/LLM integration and sampling

Clients

Each client is created by the host and maintains an isolated server connection:

  • Establishes one stateful session per server
  • Handles protocol negotiation and capability exchange
  • Routes protocol messages bidirectionally
  • Manages subscriptions and notifications

Servers

Servers provide specialized context and capabilities:

  • Expose resources, tools and prompts via MCP primitives
  • Operate independently with focused responsibilities
  • Request sampling through client interfaces
  • Must respect security constraints

MCP Primitives

MCP defines three core primitives that servers can expose:

Resources

Static or dynamic data that servers provide to clients. Examples include:

  • File contents
  • Database records
  • API responses
  • Real-time data feeds

Tools

Functions that clients can invoke on servers. Examples include:

  • Calculations
  • Database queries
  • File operations
  • API calls

Prompts

Predefined templates for common interactions. Examples include:

  • Code review prompts
  • Data analysis templates
  • Communication drafts

Getting Started

MCP is available with Python and TypeScript SDKs, making it easy to build both clients and servers. Popular AI tools that support MCP include:

The Future of AI Integration

MCP represents a significant step toward standardizing how AI applications access external systems. By providing a unified protocol, MCP enables:

  • Easier integration between AI applications and data sources
  • Reduced development complexity for AI-powered applications
  • Better security through standardized access controls
  • Ecosystem growth as more tools adopt the standard

Whether you're building AI applications, developing integrations, or simply using AI tools, MCP provides the foundation for more capable and connected AI experiences.

Ready to implement MCP in your applications? Check out our comprehensive guides on building MCP servers and integrating with popular AI tools.