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Model Context Protocol: Enhancing LLMs with Data Access

Updated on Mar 26,2025

Table of Contents

Large Language Models (LLMs) are rapidly transforming industries, yet their true potential hinges on their ability to securely and effectively access real-world data. The Model Context Protocol (MCP) is an initiative by Anthropic. It aims to address this challenge by creating a standardized protocol enabling LLMs to interact with external data sources in a controlled and secure manner. This article dives into the intricacies of MCP, its use cases, implementation, and the benefits it brings to the forefront.

Key Points

The Model Context Protocol (MCP) is an initiative by Anthropic, designed to give LLMs secure access to external data sources.

MCP is being supported by tools and other providers, but not Open AI/ChatGPT at the time of this recording.

MCP empowers LLMs like Anthropic's Claude to overcome knowledge cut-off limitations by fetching real-time information from external data sources.

Key benefits of MCP include enhanced LLM capabilities, controlled data access, and the enablement of agentic workflows.

Implementation involves setting up an MCP server that connects the LLM to the desired data sources.

Use cases of MCP are extensive, including summarizing code repositories, querying private databases, and accessing information from various file systems.

Understanding the Model Context Protocol

What is the Model Context Protocol?

The Model Context Protocol (MCP) is an emerging standard that seeks to bridge the gap between LLMs and external data repositories. As a initiative being pushed by Anthropic

, MCP is designed to provide a secure and standardized way for LLMs to access and utilize information beyond their pre-trained Knowledge Base. This protocol offers a controlled interface, dictating what data LLMs can access and how that data can be used, mitigating risks associated with unrestricted data access. By employing MCP, tools and providers can enable LLMs to become more versatile and informed without compromising data security.

While MCP isn't yet Universally adopted, its potential is significant. It's worth noting that at the time of this article's writing, OpenAI and ChatGPT do not directly support MCP, although that could change. For now, the focus is on tools like Anthropic's Claude desktop to demonstrate the practical application of the protocol.

The Problem: LLMs and Knowledge Cut-offs

LLMs are trained on vast datasets, but their knowledge is limited to the information available at the time of training. This results in a 'knowledge cut-off'

, where LLMs are unable to provide accurate or up-to-date information on recent events or changes.

For example, asking an LLM to summarize the latest commits in a software repository may result in an apology, as the LLM lacks direct access to real-time commit information. This limits the LLM's usefulness in tasks requiring current or specialized knowledge. MCP aims to solve this by enabling LLMs to dynamically fetch information from external sources, supplementing their existing knowledge.

MCP in Action: A Practical Example

Imagine a Scenario where a user asks an LLM to summarize the three latest commits in the JetBrains Kotlin repository. Without MCP, the LLM would likely fail, citing its lack of direct access to GitHub repositories and a knowledge cut-off date

.

However, with MCP enabled, the LLM can utilize a pre-configured MCP server to access GitHub's REST API, retrieve the commit information, and provide the user with a concise summary. This seamless interaction exemplifies MCP's ability to extend LLM capabilities beyond their initial training.

Components of an MCP Transaction

Key Players

To fully grasp the workings of MCP, it's essential to understand the components involved in a typical transaction:

  • Host/Client:

    This is the application or interface through which the user interacts with the LLM (e.g., Claude desktop, a web application). In the context of MCP, the host, also known as the client initiates the request.

  • Large Language Model (LLM): The AI model (e.g., Claude) that receives the query and relies on MCP to access external data. The LLM processes the user's request and interacts with the MCP server to retrieve Relevant information.
  • MCP Server: A new entity. It’s a server component that acts as an intermediary, managing the connection to external data sources and enforcing access control policies. This server provides access to GitHub's REST API. MCP servers house the logic and tools needed to fetch data from external sources. Think of it as a specialized data provider for your LLM.
  • Data Source: The external repository containing the information the LLM needs (e.g., GitHub, a private database, a file system).

The Flow of Information

The integration of these components allows for data retrieval from data repositories to LLMs:

  1. The user enters a request or query into the Host/Client interface.
  2. The Host/Client then sends the query to the LLM, requesting the user's query to be answered.
  3. The LLM knows it will need an MCP server to respond, so it calls the preconfigured MCP server, requesting specific data. Along with the original query, instructions of listing the data are also passed.
  4. The MCP Server then connects to the appropriate Data Source, retrieves the relevant data, and sends back the information.
  5. The Data source uses the MCP's server to execute the restAPI. From this step the requested commits and data are returned in a JSON object to the MCP.
  6. The MCP Server relays the data to the LLM, for example Claude, and Claude formulates the response.
  7. Finally, the Host/Client displays the enriched response to the user, therefore closing out the interaction.

The /tools Endpoint: Exposing Server Capabilities

An essential component of the MCP server is the /tools endpoint. This endpoint provides a plain text description of the server's capabilities, detailing what actions the server can perform. This allows the LLM to understand how to utilize the server effectively and what data it can access. The tools endpoint is implemented with code that facilitates data access. The importance of using proper schemas is also noted. This ensures data is accurate and well organized.

The /tools endpoint is a JSON object containing all of the data for the data sets. This JSON object provides an API.

Setting up the Model Context Protocol

Configuring Claude Desktop for MCP

To enable MCP with Claude desktop, users are required to modify the Claude_desktop.config.json file. This file dictates various operational aspects, such as Docker and NPX command for Claude to recognize these additional functionalities

.

Steps:

  1. Locate Claude_desktop.config.json in library/application/support. In the steps below, find a table to see where the file lives on different operating systems.
  2. Add information such as MCP servers, GitHub personal access tokens, and execution details to the file.
  3. Docker Command
  4. Restart Claude Desktop

Pricing and Availability

Understanding Costs

The pricing structure for using the Model Context Protocol varies depending on the tool or service you're using. Some tools may offer free tiers with limited usage, while others may have subscription-based pricing.

It's important to carefully review the pricing details of each tool and service to determine which option best fits your needs. Be sure to factor in the cost of external data sources, as these may also have their own pricing models.

Key Features of MCP

Enabling a New Generation of LLM Applications

The Model Context Protocol has opened new opportunities for LLMs to perform well. By adding tools and instructions, LLMs now support:

  • Data Integration: Providing seamless access to real-time and specialized data sources.
  • Security and Control: Allowing for managed access to data sources.
  • Extensibility: Creating specialized, agentic workflows.

Use Cases for MCP

Practical Applications of the Protocol

The Model Context Protocol allows for a wide array of benefits and use cases that have never been possible before, such as:

  • Summarizing the three latest commits. For use in JetBrains Kotlin Repository, MCP can be used to help others understand changes that have been made.

  • Private Data Access. With MCP, LLMs can query private databases, offering real-time data access without compromising security .

  • Access other Google applications. LLMs can easily and automatically access file systems from different Google platforms, offering a wide array of data.

  • Slack accessibility. MCP also allows for access to files that are stored in Slack.

| Use Case | Description |

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