Model Context Protocol Overview and Use Cases

Introduction

In November 2024, Anthropic introduced the Model Context Protocol (MCP), an open-source standard designed to streamline the integration of AI assistants with diverse data sources and tools. MCP addresses the complexity of custom integrations by providing a universal framework that enables AI systems to interact seamlessly with various applications and datasets. Similar to how HTTP standardises web communication, MCP facilitates consistent and efficient connections between AI models and external resources. This initiative aims to enhance the capabilities of AI assistants by granting them standardized access to the information they need to perform tasks effectively.

Read more at Model Conext Protocol

Overview

MCP follows a client-server architecture where:

• Hosts are LLM applications (like Claude Desktop or IDEs) that initiate connections.
• Clients maintain 1:1 connections with servers within the host application.
• Servers provide context, tools, and prompts to clients.

The idea is to allow LLMs to access external tools and data sources, enabling them to perform tasks that require more than just their internal knowledge. The protocol is designed to be flexible and extensible, allowing for a wide range of use cases. This is implemented similarly to how HTTP works, where the client sends a request to the server, and the server responds with the requested data or action. The request and response are structured in a way that allows for easy parsing and processing by both the client and server.

A number of MCP directories have emerged after MCP started receiving public attention. However, if you decide to use an MCP server written by a third party, use it with care, as this might inadvertently expose sensitive personal or confidential information.

What’s happening under the hood

When you pose a question, the following sequence occurs:

• The client transmits your query to the LLM (such as Claude).
• The LLM assesses the available tools and determines the most suitable one(s) to address the task.
• The client invokes the selected tool(s) via the MCP server.
• The tool(s) process the request and return the results to the LLM.
• The LLM interprets the results and crafts a response in natural language.
• This response is then presented to you.

Use Cases

VS Code Github Copilot

For developers aiming to enhance their AI-assisted development workflows, integrating Model Context Protocol (MCP) servers with GitHub Copilot offers a powerful solution. By configuring MCP servers, Copilot can access a variety of tools and data sources, enabling it to perform tasks such as fetching real-time information, executing code snippets, or interacting with internal systems directly within your Integrated Development Environment (IDE).

Github MCP Server

Data Analysis

Imagine you have a large dataset in a CSV file. You can use MCP to connect to a data analysis tool (like Pandas or NumPy) and ask the LLM to perform operations like filtering, aggregating, or visualizing the data. This allows the LLM to leverage the power of these libraries while still providing a natural language interface.

Code Execution

You can use MCP to connect an LLM to a code execution environment, such as a Jupyter Notebook or a Python REPL. This enables the LLM to write and run code snippets directly, making it useful for tasks like testing algorithms, running simulations, or performing complex computations. Another practical use case is integrating with infrastructure systems to retrieve and interpret live system logs. For example, an LLM-powered Slack bot using MCP could gather real-time log data, allowing IT teams to troubleshoot issues using natural language queries.

Web Scraping

Using MCP, you can connect a large language model to a web scraping tool such as BeautifulSoup or Scrapy, enabling it to interact directly with web content in real time. This setup allows the LLM to navigate websites, extract relevant data, parse complex HTML structures, and carry out data preprocessing or cleaning tasks autonomously. For instance, you can instruct the LLM to scrape specific information—like product listings, news headlines, or table data—from a webpage and return the results in a clean, structured format such as JSON or CSV, making it easier to analyze or integrate into other systems.

API Integration

With MCP, you can connect a large language model to a wide range of external APIs, including internal systems of record such as NetBox, as well as public APIs for weather, finance, social media, and more. This integration enables the LLM to retrieve real-time information, perform on-the-fly computations, and present the results in a user-friendly, natural language format. For example, you could ask the LLM to provide the current weather conditions for a particular city, pull the latest stock market data, or query internal infrastructure documentation—all without needing to manually interact with each API. This not only streamlines data access but also empowers users to make informed decisions more efficiently through conversational interfaces.

Document Processing

MCP can be used to connect a large language model to document processing tools such as OCR engines (like Tesseract) or PDF parsing libraries (like PDFMiner or PyMuPDF). This connection enables the LLM to extract text from scanned images, PDFs, and other unstructured document formats, making it possible to analyze and process complex documents programmatically. The LLM can then perform various downstream tasks such as summarization, entity recognition, or data restructuring. For instance, you could ask the LLM to locate and extract tables from a multi-page PDF report, convert them into a structured format like CSV, and even clean or annotate the data—all through natural language prompts.

Security and Privacy

The Model Context Protocol (MCP) introduces several security and privacy concerns that organizations must address when integrating AI assistants with data sources. While MCP’s architecture emphasizes a “security-first” approach—requiring explicit user or developer approval for each tool invocation and maintaining local control over data—there are inherent risks associated with its implementation.

One significant concern is the potential for malicious tool servers and compromised data integrity. Researchers have demonstrated that vulnerabilities in MCP servers can allow attackers to bypass protections, gain access to system files, and execute unauthorized commands. Additionally, the flexibility of MCP introduces risks such as prompt injection attacks, where manipulated content can override user instructions or lead to errors.

To mitigate these risks, organizations should implement fine-grained, identity-centric access controls and automated data classification to ensure both security and regulatory compliance. Federated identity management can help ensure that AI agents access data using the end user’s identity, limiting access to authorized data only. Furthermore, continuous monitoring and auditing of AI interactions with data sources are essential to detect and prevent unauthorized access or data leakage.

While MCP offers a standardized framework for AI integration, it is crucial for organizations to proactively address these security and privacy challenges to safeguard sensitive information and maintain compliance with data protection regulations.