MCP - Understanding the Basics and Building a Research Paper Management Chatbot

Introduction

Since early 2024, the rise of AI agents capable of autonomous decision-making has driven the need for a standardized approach to provide them with richer context for complex tasks. The Model Context Protocol (MCP), announced by Anthropic on November 25, 2024, emerged as a solution to this challenge. This open-source protocol acts as a universal connector between large language models and external tools, enabling them to handle increasingly complex tasks through natural language while providing a standardized framework for accessing and processing information.

This article is a summary of my comprehensive guide originally published on DEV Community, where I dive deep into building a research paper management system using the Model Context Protocol.

🤔 What is MCP?

Model Context Protocol (MCP) is an open protocol that standardizes how language models interact with tools and data sources. It uses a client-server architecture where each client (like a code editor or AI application) maintains individual connections with MCP servers. This design eliminates the need for custom integrations that previously created fragmented systems and increased maintenance complexity.

MCP introduces three core primitives:

• Tools: Functions that AI models can invoke to perform actions or retrieve dynamic data
• Resources: Read-only data endpoints that provide context (similar to GET requests)
• Prompt Templates: Pre-configured prompts optimized for specific tasks, living on the server side

🛠️ Building a Research Paper Management Chatbot

In my original article, I walk you through building a practical research assistant (a command-line chatbot) that showcases MCP's capabilities through a real-world academic use case by learning how to:

• Create an MCP server that exposes tools, resources, and prompt templates
• Connect to third-party MCP servers
• Build a custom MCP client to interact with those servers

By the end of the article, the chatbot is able to:

• Search for academic papers on arXiv
• Organize articles by research topic
• Access metadata about saved papers
• Pull information from any URL using a third-party MCP server
• Generate documents using content retrieved from external sources

🏗️ The System Architecture

The research chatbot connects multiple MCP servers:

Own Research Server: Searches ArXiv for academic papers and organizes them by topic, storing metadata locally as JSON files for persistent access across sessions.

Third-Party Filesystem MCP Server: Handles file operations like reading and writing files, developed and provided by Anthropic through their official GitHub repository.

Third-Party Fetch MCP Server: Fetches information from URIs and URLs, also developed by Anthropic and available through their official GitHub repository.

Our chatbot acts as a central host, creating MCP clients to connect with each server by:

• Server Connections: Loading configuration and establishing individual connections to each MCP server
• Tool Management: Tracking available tools across connected servers and maintaining a unified interface
• Session Handling: Creating and managing user sessions, maintaining context between interactions
• Query Processing: Routing natural language queries to appropriate servers and tools
• Connection Lifecycle: Handling graceful startup and shutdown of server connections

This client-side architecture enables the chatbot to seamlessly coordinate between multiple MCP servers while presenting a unified interface to users. The modular design also makes it easy to add new server connections as needed.

⚙️ Key Features Demonstrated

The system enables natural language interactions like:

• "search for two articles about deep learning and provide your summary of both"
• "look into https://deeplearning.ai, extract one relevant concept and research articles about it"
• "list my papers by research topic"

Behind the scenes, the AI orchestrates multiple tools to search databases, extract information, cross-reference content, and maintain organized research collections.

💡 Why MCP Matters for Developers

The research chatbot example illustrates MCP's broader implications:

Simplified Integration: Instead of building custom APIs for each external service, developers can leverage existing MCP servers or build standardized ones.

Composable Systems: Different MCP servers can work together seamlessly, enabling complex workflows that span multiple data sources and tools.

Reduced Complexity: Focus shifts from integration challenges to user experience and domain-specific functionality.

🎯 Final Remarks

In the original post, I demonstrated how MCP can be used to build AI applications that interact with external data sources. By standardizing how AI applications connect with tools and data, MCP makes it easier to build and maintain complex AI systems.

MCP represents more than a technical protocol—it's enabling a shift toward composable AI systems that amplify human insight through intelligent, connected tools.

You can find all the technical details in my GitHub repository.

📚 Resources

Full article on dev.to 👉 MCP: Understanding the Basics and Building a Research Paper Management Chatbot

Complete source code 👉 MCP Research Assistant

MCP short course 👉 MCP: Build Rich-Context AI Apps with Anthropic

Official MCP documentation 👉 MCP Documentation