Agent Experience

Model Context Protocol (MCP): The Universal Standard for AI Integration

Agent Experience Community October 20, 2025

What is the Model Context Protocol?

The Model Context Protocol (MCP) is an open standard introduced by Anthropic in November 2024 that fundamentally changes how AI systems interact with external data sources and tools. Often described as “USB-C for AI applications,” MCP provides a universal connector that enables large language models (LLMs) to access external data, tools, and services through a standardized protocol.

The Problem MCP Solves

Despite significant advances in AI reasoning capabilities, modern AI models face a critical limitation: they’re isolated from the data they need. Each new data integration typically requires custom development work, creating fragmented architectures where every data source needs its own connector. This approach becomes unsustainable as organizations scale their AI implementations across dozens or hundreds of data sources.

MCP eliminates this fragmentation by providing a single, universal protocol. Instead of building and maintaining separate connectors for each data source, developers can build against one standard, making AI integrations more sustainable, scalable, and maintainable.

Core Architecture

The Model Context Protocol consists of four main components:

1. Host

The host coordinates the overall system and manages LLM interactions. It serves as the orchestration layer that brings everything together.

2. Clients

MCP clients connect hosts to servers in 1:1 relationships. They handle the protocol negotiation and communication between the host and individual servers.

3. Servers

Servers provide specialized capabilities through the three core primitives (detailed below). They expose data sources, tools, and prompts that AI applications can use.

4. Base Protocol

The base protocol defines how all components communicate using a JSON-RPC based system. It includes:

  • Data Layer: Defines the protocol structure, lifecycle management, and core primitives
  • Transport Layer: Defines communication mechanisms and channels for data exchange

Three Core Primitives

MCP defines three fundamental primitives that servers can expose:

Resources (Application-Controlled)

Resources are data sources that provide contextual information to AI applications. Similar to GET endpoints in a REST API, resources provide data without performing significant computation or side effects.

Examples: File contents, database records, API responses, documentation

Control: Application-controlled, meaning the application decides what data to expose

Tools (Model-Controlled)

Tools are executable functions that AI applications can invoke to perform actions. Models can automatically discover and invoke tools based on context.

Examples: File operations, API calls, database queries, web searches

Control: Model-controlled, meaning the AI model can autonomously decide when to use them

Prompts (User-Controlled)

Prompts are reusable templates that help structure interactions with language models. They allow servers to provide structured messages and instructions.

Examples: System prompts, few-shot examples, conversation templates

Control: User-controlled, with clients able to discover, retrieve, and customize prompts with arguments

How MCP Works

The MCP workflow follows these steps:

  1. Connection Establishment: An MCP host initiates a connection to MCP servers

  2. Protocol Negotiation: The client and server negotiate protocol versions and exchange capability information

  3. Capability Discovery: The MCP client queries the server to discover available tools, resources, and prompts using */list methods (e.g., tools/list, resources/list, prompts/list)

  4. Context Augmentation: When a user interacts with the AI model, the host enriches the model’s context with relevant information from connected MCP servers

  5. Action Execution: The model can invoke tools, request resources, or use prompts as needed to complete tasks

Industry Adoption

MCP has achieved remarkable adoption in its first year:

Major Platform Support

OpenAI (March 2025): Integrated MCP across ChatGPT desktop app, OpenAI Agents SDK, and Responses API

Google (April 2025): Added native MCP support in Gemini 2.5 Pro API/SDK and Google Cloud’s Vertex AI Agent Development Kit (ADK)

Microsoft: Integrated MCP support within Copilot Studio

Others: Cloudflare and Vercel have also added MCP support

Ecosystem Growth

  • November 2024: ~100 MCP servers, ~100,000 downloads
  • February 2025: Over 1,000 community-built connectors
  • April 2025: 8 million total downloads
  • May 2025: Over 4,000 MCP servers available

Early Adopters

Development tool companies including Zed, Replit, Codeium, and Sourcegraph have integrated MCP to enhance their platforms. Enterprises like Block and Apollo are using MCP in production.

Available SDKs

MCP provides official SDKs in multiple programming languages:

  • TypeScript (primary development language)
  • Python
  • Java
  • C#
  • Kotlin

The protocol schema is defined in TypeScript but also available as JSON Schema for wider compatibility.

Pre-built Servers

The MCP ecosystem includes an open-source repository of pre-built servers for popular platforms:

  • Google Drive
  • Slack
  • GitHub
  • Git
  • PostgreSQL
  • Puppeteer
  • And hundreds more…

Benefits for Agent Experience

For building effective agent experiences, MCP provides several key advantages:

Standardization: One protocol to learn instead of dozens of custom integrations

Context Continuity: AI systems maintain context as they move between different tools and datasets

Scalability: Add new data sources without architectural changes

Security: Standardized security model with clear authentication patterns

Interoperability: Build once, use across multiple AI platforms and models

Community: Leverage thousands of pre-built servers and growing ecosystem

Getting Started

To start working with MCP:

  1. Visit the official documentation at modelcontextprotocol.io
  2. Explore the GitHub repository for specifications and schemas
  3. Check out pre-built servers in the MCP server repository
  4. Install the SDK for your preferred language
  5. Follow the Anthropic course: Introduction to Model Context Protocol

The Future of MCP

With adoption from OpenAI, Google, Microsoft, and other major players, MCP is rapidly becoming the de facto standard for AI-data integration. As the ecosystem continues to grow, we can expect:

  • More pre-built servers for popular services
  • Enhanced security and enterprise features
  • Deeper integration into development tools
  • Broader language and framework support
  • Industry-specific MCP server collections

MCP represents a fundamental shift in how we build AI applications—from fragmented, custom integrations to a unified, standardized approach that scales with the growing capabilities of AI systems.

License: The Model Context Protocol is open source under the MIT License

Maintained by: Anthropic, PBC with 270+ open source contributors

Share this article