A Survey of the Model Context Protocol (MCP): A Framework for Standardized, Agentic Interactions between LLMs and External Systems

📑 Table of Contents

• A Survey of the Model Context Protocol (MCP): A Framework for Standardized, Agentic Interactions between LLMs and External Systems
  • 📑 Table of Contents
  • Model Context Protocol (MCP): Empowering Agentic AI Interactions
  • Comparative Analysis: MCP vs Traditional APIs
  • MCP Architecture Overview
  • MCP Core Concepts
    • Resources
    • Prompts
    • Tools
    • Sampling
    • Roots
    • Transport Layer
  • Agentic AI & Composability
  • End-to-End MCP Workflow
  • 🧠 AI Frameworks with MCP Integration
  • Example Clients – Feature Support Matrix
  • Reference & Third-Party Servers
  • 📦 MCP SDK Information

Model Context Protocol (MCP): Empowering Agentic AI Interactions

The Model Context Protocol (MCP) provides a structured, standardized way for Large Language Models (LLMs) to seamlessly interact with external tools, resources, and systems—much like how APIs and Language Server Protocols revolutionized application integration. MCP empowers the next generation of agentic AI by enabling autonomous, secure, and context-rich interactions.

Comparative Analysis: MCP vs Traditional APIs

MCP Architecture Overview

Fig. 1: MCP Client-Server Architecture.

MCP Core Concepts

Resources

• Structured External Data: Exposes content such as text, audio, PDFs, system logs, and databases.
• Types: Text Resources (e.g., JSON, source code) and Binary Resources (e.g., PDFs, videos).
• Discovery: Via endpoints like resources/list and URI templates.

Prompts

• Reusable Templates: For standardized LLM interactions.
• Dynamic Context Injection: Supports arguments and multi-step workflows.
• Access Points: Via prompts/list and prompts/get.

Tools

Fig. 2: Tools provide active invocation using defined JSON schemas.

• Executable Capabilities: Trigger actions and external system calls.
• Definition: Each tool is defined with a name, description, input/output schema, and validation.
• Invocation: Accessed via tools/list and invoked using tools/call.

Sampling

Fig. 3: Secure and contextual LLM completions via MCP sampling.

• Server-Initiated: Sends messages to the LLM through the client.
• Human-in-the-Loop: Incorporates review/approval for secure execution.
• Control Parameters: Enables fine-tuning (temperature, token limits, etc.).

Roots

Fig. 4: Roots define operational boundaries using URIs.

• Logical Boundaries: Define scopes (directories, API endpoints) for resource access.
• Multi-Context Support: Enables composable, dynamic agent workflows.

Transport Layer

• Real-Time Communication: Utilizes secure HTTP/SSE channels.
• Reliable Messaging: Ensures structured, bidirectional interaction.

Agentic AI & Composability

MCP is a catalyst for agentic AI, enabling autonomous agents to interact, collaborate, and chain tasks dynamically.

• Dual Role Components: MCP nodes act as both clients and servers.
• Dynamic Agent Chaining: Supports complex workflows with an orchestrator triggering specialized sub-agents.
• Human Oversight: Built-in review processes ensure security and reliability.

Fig. X: Dynamic agent chaining enabled by MCP composability.

Fig. y: Multi Agent Orchestrator.

End-to-End MCP Workflow

Fig. 5: Customer Support Chatbot Workflow powered by MCP.

Workflow Highlights:

1. Capture & Send: Client submits a structured prompt.
2. Secure Transport: Data is exchanged via the Transport Layer.
3. Dynamic Invocation: Server retrieves tools and resources as needed.
4. Sampling & Review: Server requests LLM completions with human oversight.
5. Response Generation: Outputs are returned in a clear, structured format.

🧠 AI Frameworks with MCP Integration

Example Clients – Feature Support Matrix

Reference & Third-Party Servers

📦 MCP SDK Information