Model Context Protocol (MCP): An Introduction Guide

  • Date3/16/2025
  • Reading Time7 min
Model Context Protocol (MCP): An Introduction Guide

Introduction

In the ever evolving landscape of artificial intelligence and AI-driven applications, a new way of interaction with large language models (LLMs) is on the rise. The need for efficient model interaction is more crucial than ever as the rise of AI agents brings up the need for efficient interaction between systems.
The Model Context Protocol (MCP) is an emerging standard designed to streamline how AI models interact with external systems, ensuring context-aware responses and seamless integration with various applications. But what exactly is MCP, and why is it gaining traction among developers and AI enthusiasts?
This article explains what MCP is, how it works, and why it matters for the future of AI interactions.

What is the Model Context Protocol?

At its core, the Model Context Protocol is a standardized way for AI systems to access external information sources during a conversation. Think of it as giving AI models the ability to "look things up or do something" when needed, rather than relying solely on their pre-trained knowledge or the current conversation context.
Imagine you're having a conversation with an AI assistant. Without MCP, the AI can only draw from its training data (which has a knowledge cutoff date) and what you've explicitly told it in your current conversation. With MCP, the AI can dynamically access external information sources — like databases, APIs, or document repositories — to provide more accurate, up-to-date, and personalized responses.
The Model Context Protocol is an open source project by Anthropic. Their work aimed to create a unified protocol that would allow AI systems to request and receive external data in a consistent way, regardless of the underlying implementation. This standardization has been crucial for building more reliable and transparent AI assistants that can serve users with timely and accurate information.

How Does MCP Work?

The Model Context Protocol functions through a structured communication pattern between several components:
  • The User: The person interacting with the AI system (i.e. a chat interface, a SaaS application or any other option to communicate with AI).
  • The AI Model: The large language model (like GPT-4, Claude, or others) that processes user queries.
  • The MCP Server: A middleware service that interprets the model's requests for additional context and fetches relevant information.
  • Information Sources: External data repositories, APIs, or services that provide the information requested by the model.
Here's the flow of a typical MCP interaction:
  • The user sends a prompt to the AI model.
  • The model processes the prompt and determines it needs additional context to provide an accurate response.
  • The model generates a request for specific information following the MCP format.
  • The MCP server receives this request, interprets it, and queries the appropriate information sources.
  • The information sources return the requested data to the MCP server.
  • The MCP server formats the data and sends it back to the model.
  • The model incorporates this new information into its context and generates a response for the user. This process happens seamlessly and often in near real-time, creating the impression of a more knowledgeable and capable AI assistant.

The Technical Structure of MCP

Now let us take a look at how the Model Context Protocol works under the hood:

Request Structure

When an AI model needs additional context, it generates a structured request that typically includes:
json
1{
2  "queries": [
3    {
4      "provider": "github",
5      "query": "the last 10 files changed in the main branch of the example repository",,
6      "parameters": {
7        "repo_name": "example"
8      }
9    }
10  ]
11}
In this example, you can see the following:
  • queries: An array of query objects, allowing for multiple simultaneous information requests
  • provider: Specifies which information source to use (in this case "github", which likely refers to a search engine or knowledge base)
  • query: The actual query text
  • parameters: Additional configuration options e.g. the repo_name
Keep in mind, that this is a simplified version as AI models use more elaborate versions of this context request format to query external sources.

Response Format

The MCP server then returns data in a standardized format that the model can interpret:
json
1{
2  "responses": [
3    {
4      "provider": "github",
5      "status": "success",
6      "results": [
7        {
8          "filename": "app.py",
9          "commit_id": "c21100226d66741de8d56fb8351d83e5723a5e32"
10        },
11        {  
12          "filename": "requrirements.txt",
13          "commit_id": "c21100226d66741de8d56fb8351d83e5723a5e32"
14        },
15        // Additional results...
16      ]
17    }
18  ]
19}
This structured format ensures that the model can efficiently process and incorporate the new information.

Common MCP Providers

MCP implementations typically include several standard providers:
  • Search: Retrieves relevant information from search engines or knowledge bases.
  • Document: Accesses specific documents or content repositories.
  • Database: Queries structured databases for precise information.
  • API: Interfaces with external services for real-time data.
  • Vector: Performs similarity searches on vector databases (useful for semantic matching).
Each provider has its own parameters and response formats, but they all follow the general MCP structure for consistency.

Benefits of MCP

The Model Context Protocol offers several significant advantages:

1. Overcoming Knowledge Cutoffs

LLMs have a training cutoff date, after which they don't have knowledge of world events or new information. MCP allows models to access up-to-date information, effectively eliminating this limitation.

2. Reduced Hallucinations

By providing models with factual information from reliable sources, MCP significantly reduces the likelihood of "hallucinations" or fabricated information in AI responses.

3. Personalization

MCP enables models to access user-specific information (with appropriate permissions), leading to more personalized interactions without requiring all of that information to be included in the conversation context.

4. Specialized Knowledge

Models can tap into domain-specific knowledge bases and tools, allowing them to provide expert-level responses in specialized fields.

5. Transparency and Attribution

MCP implementations often include attribution for information sources, making it clear where the AI's knowledge is coming from and increasing user trust.

Real-World Examples

Let's explore some practical MCPs in action:

Example 1: Personal Assistant with Access to Calendar

User: "What meetings do I have tomorrow?"
Without MCP, the AI would have to respond with something like, "I don't have access to your calendar."
With MCP:
  • The model recognizes it needs calendar information.
  • It generates an MCP request to the calendar provider.
  • The MCP server queries the user's calendar (with proper authentication).
  • The calendar data is returned to the model.
  • The model responds with a detailed list of the user's meetings.

Example 2: Technical Support with Documentation Access

User: "How do I troubleshoot error FD-123 of this software system?"
With MCP:
  • The model recognizes this as a technical support question.
  • It generates an MCP request to search the documentation of the software system.
  • The MCP server retrieves relevant troubleshooting guides.
  • The model synthesizes this information into a helpful response, including direct quotes from official documentation.

Example 3: Research Assistant with Real-Time Data

User: "What are the latest developments in mRNA vaccine technology?"
With MCP:
  • The model determines it needs current information beyond its training data.
  • It queries a scientific research database through MCP.
  • The MCP server returns recent papers and findings.
  • The model synthesizes this information into a comprehensive summary, with citations.

Implementing MCP: Available Resources

For developers interested in implementing MCP in their own projects, several open-source resources are available:

Security and Privacy Considerations

While MCP offers powerful capabilities, it also raises important security and privacy considerations:
  • Access Control: MCP implementations must carefully manage which information sources a model can access and under what circumstances.
  • Data Minimization: Only necessary information should be retrieved and shared with the model.
  • User Consent: Users should be informed about and consent to the external data sources being accessed during their interactions.
  • Authentication: Secure authentication mechanisms must be implemented for accessing private or sensitive information sources.
  • Audit Trails: Logging of MCP requests helps maintain transparency and accountability.

The Future of MCP

The Model Context Protocol represents an important step toward more capable and trustworthy AI systems. As the protocol evolves, we can expect to see:
  • Standardization: Broader adoption of MCP standards across different AI providers.
  • Richer Provider Ecosystem: More specialized providers for different types of information and services.
  • Enhanced Reasoning: Models that can not only access information but also reason about which information to access and how to combine multiple sources.
  • User Control: More granular user controls over which information sources can be accessed and when.

Conclusion

The Model Context Protocol is transforming how we interact with AI systems by breaking down the barriers between models and the information they need. By providing a standardized way for AI models to access external information, MCP enables more accurate, up-to-date, and personalized AI experiences.
In the upcoming post, we are going to explore an example implementation of a model context protocol server. Also, we are going to explore Open AIs "answer" to the Model Context Protocol Standard.
For organizations looking to implement MCP in their AI solutions, our agency specializes in developing custom MCP servers and integrations tailored to specific business needs. We help bridge the gap between your organization's knowledge repositories and the AI systems your users interact with.
Whether you're building a customer service chatbot that needs access to your product documentation, a research assistant that requires the latest scientific papers, or an internal tool that needs to securely access company data, MCP provides the architecture to make these interactions possible.
Note: The Model Context Protocol is a rapidly evolving technology. The information in this article is current as of March 2025. For the most up-to-date information, please refer to the official MCP specification and documentation.
Author
Michael Bauer-Wapp

Michael Bauer-Wapp

CTO & Co-Founder AVIMBU

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Tags

  • AI

  • Model Context Provider

  • Anthropic

  • OpenAI