The Model Context Protocol (MCP) is becoming a game-changing standard that enables enterprises to connect their existing infrastructure directly with large language models, transforming isolated AI applications into powerful, context-aware systems that can access and interact with real-world data.
What is the Model Context Protocol?
The Model Context Protocol (MCP) is an open standard for connecting AI assistants to the systems where data lives, including content repositories, business tools, and development environments. 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.
Unlike previous fragmented approaches, it provides a universal, open standard for connecting AI systems with data sources, replacing fragmented integrations with a single protocol. Think of MCP as a universal connector that eliminates the need for custom integrations between every AI application and data source.
How does MCP work technically?
MCP operates through a client-server architecture that standardizes how AI applications communicate with external systems. MCP’s authors note that the protocol deliberately re-uses the message-flow ideas of the Language Server Protocol (LSP) and is transported over JSON-RPC 2.0.. MCP formally specifies stdio and HTTP (optionally with SSE) as its standard transport mechanisms.
The architecture consists of three core components:
- MCP Clients: AI applications that want to access external systems
- MCP Servers: Services that expose specific capabilities through the standardized protocol
- Three Essential Primitives: Tools (functions models can call), Resources (data for model context), and Prompts (templates guiding interactions)
The protocol uses JSON-RPC 2.0 messages to establish communication between: … Servers: Services that provide context and capabilities, enabling seamless data exchange without custom connectors.
What enterprise systems support MCP?
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. Major technology companies have rapidly adopted the standard:
- Microsoft: Microsoft announced the first release of Model Context Protocol (MCP) support in Microsoft Copilot Studio. With MCP, you can easily add AI apps and agents into Copilot Studio with just a few clicks.
- OpenAI: In March 2025, OpenAI officially adopted the MCP, following a decision to integrate the standard across its products, including the ChatGPT desktop app, OpenAI’s Agents SDK, and the Responses API.
- Google: Demis Hassabis, CEO of Google DeepMind, confirmed in April 2025 MCP support in the upcoming Gemini models and related infrastructure, describing the protocol as “rapidly becoming an open standard for the AI agentic era”.
How do I enable my website to communicate with LLMs via MCP?
Enabling your enterprise systems to work with MCP involves three key steps:
-
Create an MCP Server: The protocol was released with software development kits (SDKs) in programming languages including Python, TypeScript, C# and Java. Your server exposes your data and functionality through the standardized MCP interface.
-
Define Resources and Tools: Configure what data sources, APIs, and functions your AI applications can access. This architecture supports three essential primitives that form the foundation of MCP: Tools – Functions that models can call to retrieve information or perform actions · Resources – Data that can be included in the model’s context such as database records, images, or file contents · Prompts – Templates that guide how models interact with specific tools or resources
-
Deploy and Connect: You can run MCP servers directly on your development machine for testing or deploy them as distributed services across your AWS infrastructure for enterprise-scale applications.
For AWS customers specifically, By adopting MCP as a standardized protocol for AI interactions, you can: Streamline integration between Amazon Bedrock language models and AWS data services · Use existing AWS security mechanisms such as AWS Identity and Access Management (IAM) for consistent access control · Build composable, scalable AI solutions that align with AWS architectural best practices
The enterprise opportunity is significant: instead of building separate connectors for each AI tool and data source combination, MCP provides a standardized approach that scales across your entire technology stack. You can begin with a single server connecting to one data source, then expand your implementation as you validate the value and establish patterns for your organization.
By implementing MCP, enterprises can transform their existing content and data infrastructure into AI-optimized systems that provide contextual, real-time information to any compatible AI assistant – positioning them for success in the emerging intelligent web ecosystem.