🌐 Understanding Model Context Protocol (MCP)
🔍 Literal Meaning of MCP
- Model: The AI model or assistant app that initiates connection and uses external context.
Example: Claude Desktop. - Context: External data sources and functions that provide the model with relevant context and capabilities.
Examples: Claude Code, Cline. - Protocol: Standardized rules and message formats for connecting hosts, clients, and servers.
🛠️ MCP Server and Client Overview
Each MCP Server can represent a different tool provider or service, and the MCP Client can dynamically discover and route to them based on the prompt and context.
Examples of MCP Servers:
- Filesystem MCP Server
- Postgres MCP Server
- FlightRadar24 MCP Server
- SingleStore MCP Server
🔄 Step-by-Step MCP Workflow
- User Input: The user enters a prompt into the application.
- MCP Host: The prompt is sent to the MCP Host.
- MCP Client: The client sends a request to one or more MCP Servers using the MCP Protocol.
- Tool Discovery: MCP Servers respond with metadata about available tools (APIs, databases, etc.).
- Context Injection: The tools and relevant context are merged with the original prompt.
- LLM Invocation: The enriched prompt is passed to the LLM, now aware of which tools it can use.
- Tool Selection & Invocation: The LLM selects and uses the correct tool via MCP server.
- Final Output: The result is sent back to the user through the MCP Host.
🧠 Real-World Analogy
Think of MCP as a smart assistant (like Jarvis from Iron Man) that doesn’t just answer questions but also knows how to access and use tools like databases, files, or APIs to help solve your problem more effectively.
💼 Example Use Case
Scenario: A financial analyst asks the AI assistant: “Show me the quarterly trends of our top 5 products from the sales database.”
- The assistant (model) connects to the Postgres MCP Server.
- It fetches metadata (tables, columns, etc.).
- It generates the right SQL query based on context.
- It executes it, visualizes the result, and explains the trend.
✨ Benefits of MCP
- Modular: Plug-and-play architecture for tools and services.
- Context-Aware: Adds deep understanding to the LLM through real-time data.
- Dynamic Tooling: Automatically discovers and invokes the right tools.
- Scalable: Easily integrates new systems without retraining models.
🔚 Conclusion
Model Context Protocol is a foundational innovation enabling AI systems to be not just intelligent, but tool-empowered and deeply context-aware. As AI evolves, MCP-like architectures will be central to bridging the gap between raw intelligence and practical utility.