MCP – The Model Context Protocol for AI

The core problem: AI without context

Think of these examples:

• A lost driver asks a passerby, "Where am I?", and gets the reply, "In a car." Technically correct, but useless.
• A business asks an AI, "How do I increase profits?" and receives, "Reduce costs and raise prices." Again, technically correct but meaningless, what matters is how to do it.

AI needs context: access to real-time data from databases, CRM systems, internal documents, and market trends to provide actionable answers.

The old approach: ad-hoc solutions

Traditionally, businesses built custom connectors and tools to feed AI context. However, this method has flaws:

• Time-consuming: Delays in decision-making can mean missed opportunities or unmanaged risks.
• Incompatible: Multiple data sources require multiple, often clashing solutions.
• Rigid: Adding or changing data sources means rewriting code.

The modern solution: MCP (Model Context Protocol)

Developed by Anthropic in 2024, MCP is an open standard for connecting AI applications to external systems. Think of it as a USB port for AI: a universal way to plug into databases, APIs, files, and tools.

Key principles of MCP

• Universality: A single protocol for all models and environments.
• Extensibility: Supports new data sources and tools.
• Transparency: Open specification and free implementations.

MCP architecture

MCP uses a client-server model:

• MCP host: The AI application (e.g., Claude Desktop, VS Code) that manages connections to MCP servers.
• MCP client: A component that connects to an MCP server and retrieves data for the host.
• MCP server: A program that provides context (e.g., a database, file system, or GitHub repo) to clients.

Two layers of MCP

1. Data layer

   • Implements a JSON-RPC 2.0 protocol for communication, defining the structure and semantics of messages.
   • Includes:
     • Connection lifecycle management, which initializes connections between clients and servers, ensures collaboration, and terminates sessions.
     • Server functionality, which enables servers to provide:
       • Tools for executing actions.
       • Resources for contextual data.
       • Prompts for client-to-server and server-to-client interaction patterns.
     • Client functionality, which allows servers to:
       • Request data from the client for retrieval from the host.
       • Receive user requests.
       • Send messages to the client log.
     • Helper functions, which support real-time notifications and progress tracking for long-running operations.

2. Transport layer

   • Manages communication channels (e.g., stdin/stdout for local processes or HTTP streaming for remote servers) and authentication (OAuth recommended).
   • Establishes connections, formats messages, and secures communication between MCP session participants.
   • Supports two data exchange mechanisms:
     • Standard I/O transport, which uses standard I/O streams for direct communication between local processes, providing optimal performance without network overhead.
     • HTTP message streaming, which uses HTTP POST to send messages from the client to the server, with the option to stream via server-sent events. Supports standard HTTP authentication methods (tokens, API keys, and custom headers).

Key notes:

• The data layer protocol is the core of MCP, defining how context is exchanged between servers and clients.
• MCP uses JSON-RPC 2.0 as the underlying RPC protocol. Clients and servers send requests to each other and respond accordingly. When no response is required, notifications (sent as JSON-RPC 2.0 messages) enable real-time updates, such as tool changes or dynamic modifications.

MCP servers: the power of integration

An MCP server provides AI with context and capabilities via standardized interfaces. Examples:

• File servers (access to documents).
• Database servers (SQL queries).
• GitHub servers (code development).
• Megaladata servers (intelligent data analysis).

Server primitives (building blocks)

MCP servers provide three core primitives:

1. Tools: Executable modules AI can invoke based on user requests (e.g., write to a database, call an API, or edit a file). To ensure user control, hosts can require approval before execution.

2. Resources: Read-only data sources (e.g., files, databases) to provide context. Each has a unique URI (e.g., file:///path/to/document.md) and MIME type. Users interact via:

   • File browsers
   • Search/filter interfaces
   • AI-driven context suggestions

3. Prompts: Predefined templates for common interactions (e.g., "Summarize this report using data from X and Y").

MCP clients: Bridging AI and servers

• Host applications (e.g., Claude.ai, IDEs) create clients to connect to MCP servers.
• Clients enable advanced interactions via:
  1. Elicitation: Servers request missing data from users dynamically (no more errors for missing info).
  2. Root directories: Set file system boundaries for servers (e.g., file:///home/user/projects).
  3. Sampling: Servers request AI-generated text (e.g., summaries, translations) from the host’s LLM, offloading heavy text-generation tasks.

Why MCP matters: Key benefits

• Unification: Standardizes AI interactions with external systems, ending fragmentation.
• Efficiency: Reduces redundant development and improves tool quality.
• Real-world relevance: AI accesses live data, not just static training sets.
• Flexibility: Open protocol avoids vendor lock-in.

Use cases for MCP

MCP enables AI to dynamically pull context for real-world applications:

Conclusion: MCP as the future of AI integration

MCP transforms large language models from isolated knowledge bases into active participants in workflows. By standardizing how AI interacts with external data, it:

• Lowers development costs.
• Boosts AI capabilities.
• Gives users actionable, context-aware solutions.

Further reading:

• MCP-Server Megaladata: From AI's Smart Answers to Real Business Solutions
• 5 Practical Ways to Use AI in Data Management