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Last updated on Apr 15, 2025
•7 mins read
Last updated on Apr 14, 2025
•7 mins read
AI Engineer
Finding Needle from the Haystack.
The rise of large language models (LLMs) has changed how we think about AI. People now want smarter tools that can work in real time and connect with the world around them. But a powerful model alone isn't always enough. To make these tools useful, we need a way to bring in outside data, connect with tools, and stay updated.
This blog delves into how model context protocols help bridge that gap. These protocols allow AI systems to pull in the right information at the right time—like live updates or data from other apps—so AI doesn’t just sound smart; it actually works smarter.
The Model Context Protocol (MCP), introduced by Anthropic in November 2024, is an open standard designed to help LLMs access external tools and live data. In essence, MCP provides a standardized way for AI models to connect to files, APIs, databases, services, and other tools—turning LLMs into truly context-aware AI systems.
MCP enables seamless AI integration without custom code or rigid dependencies. Because of its universally applicable and adaptable nature, it's been dubbed the "USB-C for AI."
Traditional LLMs struggle to interact with external data beyond their training set. MCP fixes this by acting as a single protocol that connects AI agents with external tools, files, APIs, and data sources—standardizing how context is shared and understood.
MCP enables:
• Live tool usage from inside LLM-based applications.
• Dynamic workflows through multi-step reasoning.
• Enhanced AI assistant performance by accessing the latest data.
• Real-time insights from external systems like Google Drive, databases, and local file systems.
At its core, MCP is a client-server architecture. Here's a breakdown:
| Component | Description |
|---|---|
| Host Application | Apps like Claude Desktop, Microsoft Copilot Studio, or IDEs needing data access. |
| MCP Client | The protocol client that connects to servers and forwards requests. |
| MCP Server | A small service exposing tools or data, such as a weather API or PDF file reader. |
| External Tools | APIs, remote services, or internal systems used by the AI model. |
| File Systems | Local or cloud storage systems like Google Drive or USB-C connected drives. |
MCP acts as the glue between AI models and their required context, eliminating the need to write a new integration each time. Instead, developers can rely on tools hosted via MCP servers that handle the logic.
Here's the data flow:
The user asks the AI assistant to fetch or perform a task.
The AI model sends a request to the MCP client.
The MCP client routes the request to an MCP server.
The server interacts with data sources, APIs, or files, and returns results.
The AI consumes the context and responds intelligently.
| Advantage | Explanation |
|---|---|
| Universal Standard | Works across different AI agents, models, and environments. |
| Extensible with Multiple Tools | Supports external tools, APIs, and local data. |
| Security Built-In | Each request requires user consent and is sandboxed. |
| Reduces Developer Workload | No more custom integration for every new data source. |
| Promotes Agent Autonomy | Enables AI agents to act on external data in real time. |
• Open protocol for sharing context.
• Flexible MCP client and MCP server structure.
• Support for server-sent events to provide real-time updates.
• Seamless MCP integration with external tools and platforms.
• Enables AI integrations in a secure and modular fashion.
• Compatible with USB-C connected devices for file or media access.
• Can be deployed using pre-built servers or with custom code.
Using MCP, the Claude Desktop app can read files from a USB-C port, Google Drive, or internal file systems with a simple MCP client connection to an MCP server configured for file access.
In Microsoft Copilot Studio, integrating with GitHub becomes trivial with a model context protocol MCP setup. Developers can use external tools via a new MCP server that provides repo browsing and commit tracking.
An MCP server can be created to expose Blender’s API, enabling AI agents like Claude to manipulate 3D scenes. This demonstrates how MCP acts as a universal layer for controlling third-party tools.
The MCP specification, updated on March 26, 2025, defines how tools, data, and AI models interact consistently and securely.
It includes:
• Authentication flows.
• Tool invocation structure.
• Context lifecycle.
• Event-streaming via server-sent events.
Whether building a new MCP server or setting up an MCP client, implementation is straightforward using the C# SDK or Python libraries. Many companies now offer templates for custom integration with remote services, external data, and internal systems.
| Feature | Traditional Approach | Model Context Protocol |
|---|---|---|
| Integration Effort | High (custom code for each tool) | Low (standardized interface) |
| Flexibility | Limited | High |
| Security | Ad-hoc | Built-in |
| Community Support | Scattered | Growing open-source movement |
| Interoperability | Vendor-specific | Open standard |
• Claude Desktop now supports drag-and-drop file analysis via MCP.
• Microsoft Copilot Studio connects to GitHub and Azure via MCP clients.
• Community-run servers allow LLMs to fetch stock data, send emails, or read PDFs.
• Integration with USB-C drives is now possible for on-the-go file interactions.
The model context protocol (MCP) is not just a trend—it’s a foundational shift in how AI applications provide context. As more AI assistants and agents become capable of tool usage, the demand for MCP integration will continue to rise.
Expect to see:
• More available tools as open-source contributors expand support.
• Smarter context-aware AI that understands and adapts to real-world situations.
• Better access to different data sources, including cloud and local environments.
• Enhanced capabilities for industries from healthcare to software development.
The Model Context Protocol (MCP) has fundamentally changed how we connect AI models to tools, data, and systems. It allows developers to scale with a standardized way to exchange information, secure sensitive data, and integrate complex AI applications using familiar patterns.
With major backers like Anthropic and a booming community, MCP is poised to become the backbone of the next generation of intelligent, responsive, and secure AI integrations.
Stay tuned as the ecosystem evolves and more new capabilities are unlocked for AI-powered apps, agents, and systems.
If you're working on AI integrations, thinking about building an MCP server, or just exploring how to enhance your AI assistant, now is the time to dive into MCP.
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