I kept hearing people talk about MCPs, and honestly, I didn’t fully get what the hype was all about.
Everyone on social media and inside Discord was acting like Model Context Protocols were the best thing since sliced bread.
Some said they were building their workflows around MCPs, while others claimed it was the secret to scaling AI agents inside their IDEs or code editors.
I figured, isn’t this just a fancy word for a library? You write once, reuse everywhere, nothing new, right? At least that’s what I thought, well, I was wrong.
After digging in, I realized MCPs aren’t just libraries. They’re the connective tissue between your coding tools, your AI agents, and your codebase. And when used right, they remove friction from repetitive tasks, speed up development, and make your AI agents a lot more useful.
In this post, I’ll break down what MCPs really are, the 3 key benefits they bring, and how you can start using them in your own development process.
Table of Contents
What is MCP?
MCP (Model Context Protocol) is an open-source tool that links AI agents to outside systems, allowing AI models to easily access data and improve their performance and service delivery.
The primary goal of MCP is to facilitate the connection and interaction of AI models with various data sources and associated tools.
Currently, numerous organizations and technology specialists are actively utilizing MCP’s features to improve the functionalities of AI models.
The MCP links two essential components, enabling a flexible and efficient system. In this case, MCP servers provide a connection to data sources, while the MCP client talk to the servers. The design is adaptable and straightforward, facilitating real-time access to necessary data for AI models while permitting access restrictions.
In a nutshell, the MCP architecture is governed by 3 core components given below:
Hosts – these are basically large language models or AI agents that improve the needed connection. Instances include IDEs or Claude Desktop.
Clients – they enable the link between the servers in the hosting application.
Servers – this creates the necessary tools, environment, and prompts for users.
3 Ways to Use MCPs to Improve AI-coding
Here are three ways developers can employ MCPs to improve their coding practices with AI agents:
1. Security
Offering context frequently involves allowing an AI to access confidential information. Numerous developers are (justifiably) wary of transmitting internal data to external services or into the public domain.
MCP was created considering the best security practices. As it is an open protocol, you are able to run MCP servers on your own infrastructure, maintaining data within your firewall. You reveal only what is necessary via the protocol.
MCP’s uniform method also simplifies the auditing and enforcement of rules regarding how AI accesses data (e.g., mandating specific authentication for particular data sources).
A good use case for this is when a company uses an AI assistant to help employees find internal info like project updates or customer data. To keep things secure, they run an MCP server on their own systems.
So when an employee asks, “What’s the status of Project Zeus?” The AI assistant routes the request to the internal MCP server, which fetches the relevant information from the company’s secure database and returns it to the AI assistant, which presents it to the employee.
This way, confidential data remains within the company’s secure environment, reducing the risk of data breaches, and the company controls who can access what.
Additionally, by using an MCP server, the company can enforce strict access controls, ensuring that only authorized personnel can retrieve specific information.
2. Documentation
MCPs significantly improve the way developers interact with and manage documentation by introducing intelligent, automated, and standardized processes:
Advanced models enable precise, semantic search, which help developers find the right info faster without keyword guessing.
With MCPs, docs are intelligently classified and tagged based on content and usage context, minimizing manual input.
They also allow for Augmented Operations (AO) surface patterns in usage, gaps in coverage, and optimization opportunities.
MCPs allow for consistent metadata across docsets and makes content easier to version, localize, and extend.
A real-world example of this is how companies like Block and Apollo use MCP in their enterprise environments. They’ve implemented it to power internal AI assistants that can retrieve information from proprietary documents, CRM systems, and internal knowledge bases. This helps their employees quickly find what they need, improving productivity and decision-making.
3. Observability
When it comes to catching errors before they show up in production, MCPs like Digma MCP Server can be used to enrich AI agents with deep runtime context.
The premise is simple. Observability doesn’t need to be isolated on an APM website, encapsulated by pretty dashboards, it can be leveraged by the agent to improve its suggestions. With data on how the code actually runs, on existing errors and code flows, the agent can become smarter and more efficient at its tasks and provide more accurate feedback.
You can write a prompt like this: “Review the new /validate endpoint and let me know if there are any critical runtime issues detected in the staging env that may be related to this endpoint.”
When you enter this prompt into an AI agent like Cursor, the Digma MCP server is contacted to provide runtime context, and uses real data from the testing and CI environments to review your code based on how it actually runs and executes.
To make that happen, the Digma MCP server uses Digma’s Dynamic Code Analysis Engine. Raw observability data would be useless and simply overwhelm the agent’s context window. Digma’s engine aggregates relevant code information and makes it available to the agent to autonomously employ it when it reasons about coding tasks.
Conclusion:
With these 3 use cases for leveraging MCPs, it is evident how this technology is revolutionizing the way AI models operate.
MCP provides flexibility, allowing developers to choose between custom integrations or utilizing pre-built servers to establish an AI foundation that enhances the system’s capabilities and delivers excellent performance.
Get early access to the Digma MCP server: Here
Frequently Asked Questions
Why would I use MCP?
MCP provides a universal remote control to manage all your devices and services. Rather than remaining confined to its own world, your local or cloud LLM can now interact with and manipulate the functions of other applications securely and wisely.
Why is MCP important?
MCP is important because it provides a universal, open standard for connecting AI systems to data sources, substituting scattered integrations with one protocol. The outcome is a more straightforward, dependable method for providing AI systems with the data they require.
What are the benefits of MCP?
It allows developers to expose their services and APIs to numerous AI clients through the application of a single protocol. It enables bidirectional communication, tool identification, and a comprehensive array of primitives that both servers and clients can utilize.
