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

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📑 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

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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.

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Comparative Analysis: MCP vs Traditional APIs

Feature	Traditional APIs	Model Context Protocol (MCP)
Tool Usage	Manual, bespoke code	Dynamic, standardized calls
Prompt Interaction	Basic text-based	Structured and context-aware
Context Handling	Limited	Integrated, built-in
Discovery	Manual	Dynamic and introspective
Security	Varies widely	Enforced mechanisms

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MCP Architecture Overview

Fig. 1: MCP Client-Server Architecture.

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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.

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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.

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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.

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🧠 AI Frameworks with MCP Integration

Framework	Description	Integration Link
LangChain	Lightweight wrapper for making Anthropic MCP tools compatible with LangChain and LangGraph.	langchain-ai/langchain-mcp-adapters
CrewAI	Provides an MCP server to manage and trigger deployed CrewAI workflows.	crewAIInc/enterprise-mcp-server
LlamaIndex	Integration for connecting LlamaIndex tools with MCP tools using llama-index-tools-mcp.	llama-index-tools-mcp

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Example Clients – Feature Support Matrix

Client	Resources	Prompts	Tools	Sampling	Roots	Notes
Claude Desktop App	✅	✅	✅	❌	❌	Full support for all MCP features
5ire	❌	❌	✅	❌	❌	Supports tools.
BeeAI Framework	❌	❌	✅	❌	❌	Supports tools in agentic workflows.
Cline	✅	❌	✅	❌	❌	Supports tools and resources.
Continue	✅	✅	✅	❌	❌	Full support for all MCP features
Cursor	❌	❌	✅	❌	❌	Supports tools.
Emacs Mcp	❌	❌	✅	❌	❌	Supports tools in Emacs.
Firebase Genkit	⚠️	✅	✅	❌	❌	Supports resource list and lookup through tools.
GenAIScript	❌	❌	✅	❌	❌	Supports tools.
Goose	❌	❌	✅	❌	❌	Supports tools.
LibreChat	❌	❌	✅	❌	❌	Supports tools for Agents.
mcp-agent	❌	❌	✅	⚠️	❌	Supports tools, server connection management, and agent workflows.
oterm	❌	❌	✅	❌	❌	Supports tools.
Roo Code	✅	❌	✅	❌	❌	Supports tools and resources.
Sourcegraph Cody	✅	❌	❌	❌	❌	Supports resources through OpenCTX.
Superinterface	❌	❌	✅	❌	❌	Supports tools.
TheiaAI/TheiaIDE	❌	❌	✅	❌	❌	Supports tools for Agents in Theia AI and the AI-powered Theia IDE.
Windsurf Editor	❌	❌	✅	❌	❌	Supports tools with AI Flow for collaborative development.
Zed	❌	✅	❌	❌	❌	Prompts appear as slash commands.
SpinAI	❌	❌	✅	❌	❌	Supports tools for Typescript AI Agents.
OpenSumi	❌	❌	✅	❌	❌	Supports tools in OpenSumi.
Daydreams Agents	✅	✅	✅	❌	❌	Support for drop-in servers to Daydreams agents.

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Reference & Third-Party Servers

Server	Description	Link
AWS KB Retrieval	Retrieves data from the AWS Knowledge Base using Bedrock Agent Runtime.	GitHub
Google Drive	Enables file access and search within Google Drive.	GitHub
Google Maps	Provides location services, directions, and place details.	GitHub
Redis	Interacts with Redis key-value stores for caching and data management.	GitHub
PostgreSQL	Offers read-only database access with schema inspection.	GitHub
Cloudflare	Deploys, configures, and interrogates resources on the Cloudflare platform.	GitHub
Stripe	Integrates with Stripe API to manage payments, customers, and refunds.	GitHub
Neo4j	Provides interaction with Neo4j Graph Database for graph-based operations.	GitHub
Apify	Leverages pre-built cloud tools to extract data from websites and APIs.	GitHub

🌐 Source Repositories
🔗 modelcontextprotocol.io/clients
🔗 github.com/modelcontextprotocol/servers
🔗 mcp.composio.dev
🔗 mcp.so

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📦 MCP SDK Information

SDK Name	Language	Description	Link
TypeScript MCP SDK	TypeScript	A comprehensive SDK to build MCP servers and clients in TypeScript.	GitHub
Python MCP SDK	Python	A robust SDK for implementing MCP servers and clients in Python.	GitHub
Java MCP SDK	Java	SDK for building and managing MCP infrastructure in Java.	GitHub
Kotlin MCP SDK	Kotlin	Kotlin-native SDK for MCP client-server communication.	GitHub
C# MCP SDK	C#	SDK for .NET/C# developers to integrate MCP functionality.	GitHub