Artificial intelligence is evolving rapidly, and one of the main challenges for developers and solution architects is enabling AI models to interact effectively with external tools, data sources, and APIs. The Model Context Protocol (MCP) addresses this challenge by acting as a bridge between AI models and external services, creating a standardized communication framework that enhances tool integration, data accessibility, and AI reasoning capabilities.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open standard developed by Anthropic that aims to standardize how AI applications, especially those based on large language models (LLMs), interact with external tools and data sources.
MCP provides a common interface that allows AI applications to access diverse data sources and tools in a standardized way, eliminating the need for custom integrations for each application-data source combination. This standardization facilitates interoperability and reduces the complexity of developing AI solutions.
From an M×N Problem to an M+N Solution
One of the biggest obstacles in developing AI agents is enabling them to interact efficiently with multiple external systems—from enterprise APIs to tools like GitHub, Slack, or Notion—without requiring a vast number of specific integrations that hinder scalability.
Traditionally, each AI application had to connect individually to every system it needed to use. This creates what’s known as an M×N problem, where M is the number of AI applications or agents, and N is the number of external tools. For example, 5 assistants connected to 10 systems would require up to 50 separate integrations, many of them redundant, inconsistent, or hard to maintain.
MCP transforms this scenario into an M+N model. It proposes a structured and standardized approach that converts this exponential problem into a much more manageable and modular architecture:
- Each external system (CRM, project manager, storage, etc.) implements an MCP server that exposes its capabilities (resources, tools, prompts) following the protocol specification.
- Each AI application or agent implements an MCP client that connects to those servers and acts as a bridge between the model and external services.
With this architecture, systems and applications don’t need to know each other or create ad hoc integrations. MCP becomes the standardized intermediary layer that allows them to interoperate securely and predictably.
This approach drastically reduces complexity: instead of creating M×N integrations, only M clients + N servers are needed, which:
- Decreases development time and cost.
- Improves system maintainability.
- Accelerates the adoption of new tools or agents.
- Facilitates component reuse across multiple contexts.
We can draw a clear parallel: MCP is to AI what USB is to hardware. Just as USB eliminated the need for a different cable for every peripheral, MCP standardizes the connection between AI agents and the digital ecosystem, establishing a common, extensible protocol that facilitates the development of more powerful, secure, and modular solutions.
How does Model Context Protocol work?
MCP defines a client-server architecture composed of three main actors:
- Host: The application the user interacts with (such as Claude Desktop, an IDE, or a custom agent).
- MCP Client: A component inside the host that connects to a specific MCP server.
- MCP Server: Exposes tools, resources, and prompts via a standardized API that the AI model can use.
Core Primitives
MCP is built around three essential primitives provided by MCP servers:
- Tools (model-controlled): Functions the LLM can use to perform actions, such as calling a weather API or querying a database.
- Resources (application-controlled): Static data sources, similar to GET endpoints. They provide information without side effects.
- Prompts (user-controlled): Predefined templates that structure how tools or resources are used.
Typical Interaction Flow in MCP (Step-by-Step)
- Initialization: When the host application starts (e.g., an IDE or desktop assistant), it activates MCP connectors (also known as MCP clients) that connect to the available MCP servers. An initial handshake is performed to verify protocol versions and supported capabilities.
- Capability Discovery: Each MCP connector queries its corresponding server to discover which tools, resources, and prompts are available. The server responds with a structured description.
- Context Provision: The host organizes the retrieved information (e.g., serializing tools to JSON format) and makes it available to the LLM, either for user presentation or model-driven execution.
- Tool Usage: During execution, if the LLM decides a tool is needed (e.g., “What tasks are open in Asana?”), the host instructs the corresponding MCP connector to run that function.
- Execution and Response: The MCP server performs the requested action (e.g., queries the GitHub API) and sends the result to the connector, which forwards it to the host. The updated information is then integrated into the LLM’s context, allowing it to generate a relevant and informed response.
Why So Much Interest in MCP?
There are both technical and strategic reasons for MCP’s growing adoption:
- Designed for AI Agents: Unlike OpenAPI or GraphQL, MCP was built with modern AI agents in mind, clearly structuring Tools, Resources, and Prompts.
- Detailed Specification: It offers a robust and open specification, unlike many alternatives.
- Built on Solid Foundations: MCP draws from the Language Server Protocol (LSP) and uses JSON-RPC 2.0.
- Expanding Ecosystem: Anthropic released not only the spec but also SDKs (Python, TypeScript, Java), testing tools like MCP Inspector, and ready-to-use reference servers (Slack, Git, etc.).
- Backed by Major Players: OpenAI, Cursor, Windsurf, and others have already integrated MCP into their platforms.
Security, Authentication, and the Road Ahead
Like any emerging technology with the potential to become a standard, MCP is not static: it is actively evolving to meet real-world developer needs and the security and scalability demands of large-scale adoption.
The March 2025 protocol update introduced key enhancements in several critical areas:
- Strong Authentication with OAuth 2.1: MCP now mandates OAuth 2.1 for authenticating remote HTTP servers, aligning with modern web and API security practices.
- More Efficient, Flexible Transport: MCP replaces Server-Sent Events (SSE) with Streamable HTTP, enabling real-time communication and support for JSON-RPC batching. This improves performance and simplifies high-frequency integrations.
- Richer Tool Metadata: Tools can now carry additional annotations describing their expected behavior, such as read-only or destructive. This enables LLMs to make safer, more informed decisions when using them.
The community behind MCP—driven by Anthropic and supported by open contributions—demonstrates a clear commitment to the protocol’s evolution. Updates reflect not just technical refinement but a broader vision to build a secure, efficient, and scalable foundation for the next generation of intelligent agents.
With these advances, MCP stands not only as a practical technical solution, but as a mature architecture ready to become the dominant standard for AI-to-system connectivity.
Conclusion
The Model Context Protocol represents a major advancement in how AI systems interact with the external world. By providing a standardized method for accessing and using external data sources, MCP enables AI applications that are more capable, accurate, and context-aware.
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