Unlocking AI’s Full Potential: Understanding the Model Context Protocol
Discover how the Model Context Protocol (MCP) enables AI applications to access data, execute actions, and learn from feedback in real-time. Learn about the four key components that make AI systems more powerful and adaptable.
Core MCP Concepts: Resources, Tools, Prompts, and Sampling
Imagine giving your AI assistant not just a brain, but hands and feet too. What if your AI could not only think about data but also retrieve it, transform it, and learn from how people interact with it? This is exactly what the Model Context Protocol (MCP) aims to accomplish. While large language models (LLMs) have impressive reasoning capabilities, they’re often limited by their inability to access real-time data or perform actions in the world. MCP bridges this gap by providing a standardized framework that enables AI to interact with data, perform tasks, and continuously improve through feedback.
In this article, we’ll explore the four pillars of MCP that make this possible: Resources, Tools, Prompts, and Sampling. Whether you’re building a customer service chatbot, a research assistant, or any AI-powered application, understanding these concepts will help you create more powerful and flexible solutions.
The Four Pillars of MCP
MCP consists of four interrelated components that work together to create intelligent, adaptable AI applications:
Resources: Standardized gateways to various data sources
Tools: Functions that enable AI to take actions
Prompts: Templates that guide AI behavior
Sampling: Mechanisms for collecting feedback and improving AI over time
Let’s dive into each one to understand how they work and how they can be implemented in your AI applications.
Resources: The AI’s Access to Data
What Are Resources?
In MCP, a Resource represents any data source an AI agent can access. Think of databases, filesystems, APIs, cloud storage — any place where information lives. MCP abstracts away the complexities of different data storage systems, providing a unified interface for AI to interact with data.
This abstraction is powerful because it means your AI doesn’t need to know the specific details of how to connect to a PostgreSQL database versus a MongoDB database versus a REST API. It simply uses a standardized protocol.
How Resources Work: The Power of URLs
MCP uses Resource URLs to identify and access different data sources. These URLs follow a specific structure:
For example:
Each URL component serves a specific purpose:
Scheme: Identifies the Resource type (database, file, api, etc.)
Authority: Specifies the Resource server or service
Path: Indicates the location within the Resource
Query Parameters: Provides optional configuration
Resource Implementation Example
Let’s look at how you might implement access to a customer database:
This code demonstrates how MCP Resources enable precise data retrieval without exposing underlying implementation details. You simply specify what data you want through the URL, and the MCP client handles the rest.
Tools: Enabling AI to Take Action
What Are Tools?
While Resources allow AI to access data, Tools enable it to perform actions. In MCP, a Tool is a function that an AI agent can execute — think of it as giving your AI “hands” to interact with the world.
Tools can be anything from simple calculators to complex workflow engines. They might perform mathematical operations, search for information, send notifications, or interact with external services.
Tool Invocation Process
When an AI agent wants to use a Tool, it follows a specific protocol:
Discovery: The AI learns what Tools are available
Parameter Preparation: The AI prepares the necessary inputs
Invocation: The AI sends a request to execute the Tool
Execution: The MCP server runs the Tool
Result Handling: The AI receives and processes the results
Simple Tool Example: Rectangle Area Calculator
Let’s implement a simple Tool that calculates the area of a rectangle:
And here’s how a client might invoke this Tool:
Advanced Tool Example: Sentiment Analysis
Tools can also integrate with sophisticated AI services. Here’s an example of a sentiment analysis Tool:
Complex Document Retrieval System
One particularly powerful application of Tools is in document management and retrieval. Here’s an example of a Tool that implements an advanced document retrieval system:
This document retrieval system goes far beyond simple search. It ingests a large corpus of documents, stores metadata and extracted fields in a graph database, and keeps document paragraphs in a vector database with links to related sections and pages. It combines multiple search approaches — reranking, keyword search, BM25, HyDE, and vector similarity search — to deliver precise results.
What’s powerful about MCP is that this complex system is exposed through a simple Tool interface. Your AI agent doesn’t need to understand the implementation details; it just needs to provide a query and filters to get relevant results. This abstraction allows you to continuously improve the underlying retrieval system without changing how AI agents interact with it.
Prompts: Guiding AI Behavior
The Power of Prompts in MCP
Prompts are structured instructions that guide AI models within MCP. Think of them as precise search queries for an AI “search engine” or detailed instruction manuals for an expert. Well-crafted prompts are essential for eliciting accurate, relevant responses.
Prompts serve two key functions:
Guiding Behavior: They tell the AI what to do and how (e.g., desired tone, format)
Extracting Information: They specify what information is needed from the AI’s response
In MCP, prompts are often templates with placeholders that get filled in dynamically. This allows for flexibility and reuse.
Prompt Templates and Formatting
Here’s a simple example of a translation prompt template:
At runtime, the placeholders ({source_lang}, {target_lang}, and {text}) are replaced with actual values.
The art of designing effective prompts is called prompt engineering. It involves understanding how AI models interpret language and structuring requests to achieve specific goals.
Document Summarization Example
Let’s see how we might implement document summarization using MCP prompts:
Here’s an asynchronous version of the same function:
These examples show how prompts can be combined with Tools to create powerful document processing capabilities.
Sampling: The AI Feedback Mechanism
Understanding Sampling in MCP
Sampling in MCP is a sophisticated mechanism that enables AI systems to collect, analyze, and utilize feedback data. It’s what transforms static AI systems into dynamic, evolving agents that get better over time.
Sampling serves two main functions:
Content Generation: MCP sampling allows servers to request LLM completions from clients, enabling dynamic content generation.
Feedback Collection: It involves collecting and analyzing user interactions (e.g., ratings, comments) to improve model performance.
The Science of Feedback Loops
AI feedback loops involve several interconnected components:
Output Generation: The AI produces an output
Evaluation: The output is evaluated (by humans or metrics)
Signal Processing: The evaluation is converted into a learning signal
Model Update: The model adjusts based on the learning signal
Iteration: The process repeats, creating continuous improvement
Sampling Strategies
Different situations call for different sampling techniques:
Data Collection Methods
Passive Sampling: Automatically collecting data during normal operation
Active Sampling: Deliberately soliciting feedback from users
Implicit Sampling: Inferring feedback from user behavior
Explicit Sampling: Directly asking users for ratings or comments
Selection Strategies
Random Sampling: Data points are selected randomly
Stratified Sampling: Samples are drawn from subgroups to ensure balanced representation
Uncertainty-Based Sampling: Prioritizes data points the AI is most uncertain about
Information-Directed Sampling: Balances exploration and exploitation
Let’s implement some of these strategies:
Random Sampling Example
Stratified Sampling Example
Comprehensive Feedback Pipeline
For production systems, you might implement a more comprehensive feedback pipeline:
Bringing It All Together: The Interplay of MCP Components
The true power of MCP emerges when Resources, Tools, Prompts, and Sampling work together. Let’s see an example of a simplified research assistant that finds, summarizes, and extracts key information from research papers:
This example demonstrates the seamless integration of MCP’s core components:
Resources provide access to research papers
Tools process the papers through summarization and keyword extraction
Prompts guide the AI’s behavior when generating summaries and extracting keywords
Sampling collects feedback on the results for future improvements
Conclusion: The Future of AI with MCP
The Model Context Protocol provides a comprehensive framework for building intelligent, adaptable AI applications. By standardizing how AI systems access data, perform actions, generate responses, and learn from feedback, MCP enables the development of more powerful and flexible AI solutions.
The key advantages of using MCP include:
Modularity: Each component can evolve independently
Standardization: Consistent interfaces simplify development
Flexibility: Easy to add new capabilities or data sources
Continuous Improvement: Built-in feedback mechanisms enable learning
As AI continues to evolve, frameworks like MCP will be increasingly important for managing the complexity of AI systems while maximizing their capabilities. By understanding and implementing the four core concepts — Resources, Tools, Prompts, and Sampling — developers can create AI applications that not only think but also act and learn in the world.
Whether you’re building customer service chatbots, research assistants, document management systems, or any other AI-powered application, MCP provides the architectural foundation for success. The future of AI isn’t just about smarter models — it’s about creating ecosystems where those models can access information, take actions, and continuously improve through feedback. MCP is a significant step toward that future.
Glossary of Key Terms
MCP (Model Context Protocol): A standardized framework for AI systems to access data, perform actions, generate responses, and learn from feedback.
Resource: A standardized gateway to a data source (database, filesystem, API) accessible via the MCP protocol.
Resource URL: A URL format for addressing and accessing Resources in MCP (e.g., database://my_database/customers?api_key=KEY).
Tool: An executable function accessible through MCP that allows AI agents to perform actions.
Prompt Template: A structured instruction with placeholders that can be filled dynamically to guide AI model behavior.
Prompt Engineering: The practice of designing effective prompts for AI models to elicit optimal responses.
Sampling: The MCP component that enables two-way communication through content generation and feedback collection.
Feedback Loop: A cycle where AI outputs are evaluated, feedback is collected, the model is updated, and new outputs are generated.
If you would like more details checkout this chapter in this book. The chapter goes into a lot more details and explanation.
About the Author
Rick Hightower is a seasoned technologist and AI expert with extensive experience in software development and AI. As a prolific writer and thought leader in the tech industry, he regularly shares insights about artificial intelligence, software architecture, and emerging technologies through his articles and publications.
His work focuses on making complex technical concepts accessible to developers and technology professionals, particularly in the areas of AI implementation, and software development best practices. Rick maintains an active presence in the tech community through his writing, speaking engagements, and collaborative projects.
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Expertise: Artificial Intelligence, Data Engineering, Software Architecture, ETL, Event driven software / streaming, Cloud Computing