🧱 Key Components of MCP: Clients, Tools, Servers & Resources

To understand the power of the Model Context Protocol (MCP), you need to get familiar with its foundational components. MCP is not just a protocol—it’s an architectural framework built around four core concepts:

Clients, Tools, Servers, and Resources.

Together, these components provide the structure that makes modular, agentic AI possible. Let’s explore each one in depth.

🧑‍💻 Clients

A client is typically an LLM-powered agent, chatbot, or orchestration layer that interacts with the MCP server.

Responsibilities:

  • Initiate sessions
  • Fetch and update context
  • Call tools through the server

Examples:

  • A chatbot UI built with React that sends requests to the MCP server
  • A LangGraph node that acts as a tool-aware memory-driven LLM client

Why Clients Matter:

Clients are the active agents. They drive the session by interacting with tools and managing context.

🧰 Tools

Tools are functions or services exposed to the client via the MCP server.

Responsibilities:

  • Provide callable APIs (e.g. run_python, search_web, summarize_text)
  • Return structured responses with metadata

Requirements:

  • Must expose methods following JSON-RPC 2.0 conventions
  • Optionally include schema for inputs and outputs

Example Tool Entry:

{
  "tool_id": "run_python",
  "description": "Execute Python code and return the result.",
  "methods": ["execute", "get_logs"]
}

Why Tools Matter:

They extend the capabilities of the model—enabling computation, search, and more without hardcoding those skills into the LLM.

🧠 Resources

Resources are contextual state objects used to store and share memory.

Types of Resources:

  • Logs (chronological interaction history)
  • Memory (long-term notes, embeddings)
  • Scratchpads (temporary computation space)

Stored As:

  • JSON objects associated with session IDs
  • Addressable via API endpoints (e.g., /resource/{session_id}/memory)

Why Resources Matter:

They are the long-term memory and shared blackboard for agentic systems. Agents can read, write, and summarize them across sessions.

🧭 The MCP Server

The MCP server is the backbone that connects clients, tools, and resources.

Responsibilities:

  • Manage authentication and routing
  • Store and retrieve context
  • Proxy tool calls and return results

Common Implementations:

  • FastAPI servers with a /context, /tool, and /resource route structure
  • Docker containers deployed in cloud or edge settings

Why Servers Matter:

The server is the middleware brain—handling the plumbing of modular AI interactions.

🔄 How They Work Together

Example Workflow:

  1. Client initiates session and sends query
  2. Server retrieves session context from resources
  3. Client selects a tool (e.g. search_web) to use
  4. Server invokes the tool via JSON-RPC
  5. Tool returns result → server updates resource
  6. Client receives result and continues reasoning

This is a context loop—the foundation of memory-driven, multimodal, and multi-tool AI workflows.

📊 Visual Summary

[Client] ⇄ [Server] ⇄ [Tool]
              ⇅
          [Resources]
  • Client: The brain
  • Tool: The muscle
  • Server: The spine
  • Resource: The memory

✅ Final Thoughts

MCP’s architecture separates concerns elegantly:

  • Clients reason
  • Tools act
  • Servers route
  • Resources remember

This modularity enables the plug-and-play AI future—where developers can swap models, scale infrastructure, and evolve workflows without rewriting the whole stack.

👉 Up next: “Why MCP Matters: Enhanced Contextual Understanding for LLMs”