As Large Language Models (LLMs) evolve into full flown agents, we need a universal implementation standard. In our industry’s current state, AI is developing faster than standards can keep up. For AI to fulfill its intended purposes in today’s society, we need a standardized approach. In the mid 2010s, REpresentational State Transfer (REST) APIs created a universal web standard for data pipelines and developement. We’re now experiencing that same crossroad in the AI industry.
When you’re finished with reading this article, you’ll be able to answer the following questions.
- What is Model Context Protocol (MCP)?
- What problems can it solve?
- How is it currently being used?
- How will MCP affect the future of AI?
The challenge of fragmented AI data access
As mentioned above, REST APIs have revolutionized web development. If you’re unfamiliar with the concept of REST, IBM has an article explaining the topic in detail here. REST brought us datastreams tied to different endpoints and HTTP methods. Mozilla can teach you about HTTP here.
That said, REST APIs in their current form are built for systems of the past. With a REST API, an actual developer reads the documentation and then creates a plan based on the endpoints they need to use. This can work with AI, but if we want to give AI models agency, we need take it a step further. An application programming interface (API) for AI models needs to do the following.
- Connect to a model easily.
- List the tools available to the model.
- Tell the model how to use these tools.
- Explain to the model what each tool does and when to use it.
The current RESTful standard simply isn’t suited for these requirements. In the new development world, you won’t write code for a case-by-case basis with if/else chaining. You plug your model into a data stream and tell it what to do using natural language.
Introducing the Model Context Protocol (MCP): A vision for unified AI data interaction
Model Context Protocol (MCP) attempts to bridge the gap between REST APIs and LLMs. AI development with REST often requires hooking an LLM into many different REST APIs — each with their own custom endpoints. This then requires significant boilerplate for each tool the model needs access to. MCP is arising as a universal standard to remove this custom boilerplate approach.
When a model connects to an MCP server, the server does the following.
- Tells the model a list of available tools.
- Explains where each tool should be used.
- Tells the model how to use each tool.
MCP is used to plug a model into a toolset. It then tells the model how and when to use each tool. This is the leap required for us to design and deploy software using natural language.
The universal benefits of MCP integration for AI development
Widespread adoption of MCP allows AI developers and teams to focus on functionality instead of boilerplate. MCP does for AI what GitHub and package managers did for software in the past. Users and developers get a universal standard that provides the following benefits.
- Simplified Integration: Teams can focus on function instead of boilerplate and glue code. Instead of building a new software architecture for each integration, you can create agents with a plug and play style interface.
- Faster Development and Deployment: As mentioned above, this standard removes boilerplate. We can even add toolchains, compilers and CI pipelines into our agents — your agent itself can become a deployment pipeline with full-stack execution. Software jobs that took weeks can now be finished in hours.
- Reduced Cognitive Load: When developers don’t need to focus on custom pipelines and integrations, everyone is less stressed. Your team can focus on problem solving instead of plumbing.
- Enhanced Interoperability: MCP is designed for interoperability. Toolsets can be shared and connected across any AI agent. Right now, you can share AI tools made for any LLM with tools like n8n. When one person publishes a tool for use by agents, everyone gains access to it.
- Maintenance and Scalability: LLMs now hold a significant portion of our actual software logic. If a provider or developer changes their backend, the agent is the only thing that needs changed. Update your agent’s toolset via MCP and you don’t need to worry about “down for maintenance.”
MCP makes AI systems easier to build, faster to ship and safer to scale.
MCP in action: Streamlining access to web data
Just like modern humans, the vast majority of modern AI agents are dependent on web access and various other software applications and tools. Right now, there are a number of MCP tools already changing our world. MCP abstracts away much of the low level stuff when dealing with APIs. Instead of hardcoding logic, you can now tell the machine to “do the thing.”
MCP servers are already reshaping the following paradigms by eliminating brittle code and using LLMs to implement sound logic that adjusts to backend changes easily.
Web Data
We can connect our LLMs to tools like Bright Data MCP Server for real-time web access and get real-world web data with ease. When your agent has access to the web, it can read and react in real-time — just like you do. No matter your needs — Wikipedia, Weather Data or anything else — your agent can stay up to date and react based on the information.
GitHub Integration
GitHub MCP Server gives your agent real-time access to GitHub. You can use an agent to automate repo maintenance, issues, pull requests, code security and experimental features as well. As tools like this continue to evolve, we’ll see automation overtake the entire development workflow — from brainstorming to DevOps.
Cloud Storage
LLMs are already getting access to our files stored in the cloud. Google Drive MCP Server lets your agent access Google Drive files for quick analysis of sheets, docs, photos and drawings. When your business depends on Google Drive, your agents can handle files of all shapes and size. There are already countless other MCP servers that give your agent file access as well.
You can find a much fuller list of MCP servers available here on GitHub.
DevOps
DevOps is often one of the biggest pain points in software development. Solo developers often need to reference guides on servers they touch only a few times a year. When something breaks — even temporarily — it feels catastrophic. With tools like AWS MCP Server, your AI can follow AWS best practices when managing and deploying to your cloud infrastructure. Access databases, Aurora and even create synthetic data.
Payment Processing
Payment processing is also a large pain point for many teams. it often involves webhooks and calls to a processor like the Stripe API or perhaps a blockchain explorer for crypto. The Stripe Agent Toolkit, eliminating many of the pain points when adding payment processing to your application.
Empowering AI applications through standardized data access
Applications are evolving rapidly. Rapid software development first gained traction in the 1990s and 2000s. In this development paradigm, you need to fail fast and iterate quickly. MCP-powered AI allows us to run this process exponentially faster than ever thought possible.
We’re already beginning to see the rise of the following products in the real world and MCP is making it happen even faster.
- Autonomous Research Agents: In seconds, an AI model with web access can perform research that would’ve taken days or even weeks of websurfing. This is research that would’ve taken months or even years before the internet redefined libraries. MCP allows the model to hook into the tools required for this.
- Self Updating Knowledge Bases: With MCP, agents can populate internal dashboards and even backend databases. Rather than building a pipeline yourself, you can now give an AI agent access to both your data sources and databases. The agent itself can check and update the database without the need for complex integrations and pipelines.
- Intelligent Assistants With Persistent Memory: We’re already seeing AI-powered help desk agents. MCP gives agents support for context and memory. As time goes on, expect help desk agents that know and understand you and your ticket history.
- Resilient Multi-Vendor Systems: Imagine a bot hooked into multiple stock or crypto exchanges. If it fails to fetch a price, it simply defaults to the next one. This isn’t limited to the financial industry. MCP allows your agent to hook into multiple pipelines at once. Then it uses its own discretion to handle the data.
The technical advantages of MCP for AI systems
MCP improves software in ways we haven’t even realized yet. Currently, we’re seeing improvements in architecture, reliability and efficiency. However, even as this article gets written, there are probable other paradigms emerging. MCP is already improving how we build, debug and deploy systems — and we’ve only scratched the surface of what’s possible.
Unified Data Interface
LLMs can take in just about any unstructured data and output it as structured data. This is incredibly useful. No matter the source, agents can quickly convert unstructured data into formats like the following.
- JSON
- CSV
- Excel
- SQL
- BSON
- XML
The formats above are just examples. An MCP can define its required input format and AI models can fit your input data to it within seconds.
Abstraction Layers and Custom APIs
Just as LLMs can output structured data, they can also generate structured data. When connected to an API, you’re no longer limited to JSON or XML. You can hook your tools into a machine that spits out word documents, images and even videos. The sky is the limit. No matter your formatting requirements, LLMs can take in data and convert it to your desired specifications.
Imagine a machine that reads raw API data and creates a video news summary of that data. This is where we’re headed.
Interoperabiltiy and Cross Referencing
Your AI agent can cross reference multiple data sources. We’re already seeing primitive versions of this with LLM-integrated search from models like ChatGPT, Gemini and Grok. The newer iterations of this will become more specialized while maintaining contextual understanding.
Imagine running a car factory and you need a very specific part — only available from a handful of suppliers globally. An AI agent can query these providers all concurrently to see which parts are in stock and when others will become available. This process used to take a real phone call to each distributor and sometimes still does when databases are incorrect. Nowadays, you don’t just automate the database check. MCP allows you to automate the “phone call.”
The future of MCP: Towards a more interoperable AI ecosystem
In the coming years, we’ll see AI agents growing in the following ways. Each of them provides unique abilities for automation and scaling.
- Ecosystem Growth: Just as REST became an industry standard in the mid-2010s, we’re already seeing rapid offerings in MCP servers. You can view a giant list of MCP servers from the MCP GitHub here.
- Model-Native Toolchains: Currently, humans setup the wires and pipes to hook the LLMs into tools. As time goes on, we’ll likely be getting models that create their own integrations. Models will be able to build their own integrations with limited human intervention.
- Vendor Agnostic Agents: We’re already seeing a bit of this. You can already build an agent that uses Bright Data’s scraping APIs whenever a basic Google search fails. When you go into the hardware store for a hammer, your hands aren’t compatible with only one hammer. Now we’re giving AI the power to hold the hammer — figuratively of course — and it won’t care who made the hammer as long as the tool works.
- Zero-Integration Deployment: In its old form, software development started with an idea. Then, it moved to research and planning. Finally, you’d move to coding. After development, you’d go through a testing period and deploy your code. You can now tell a model “make me a [insert app here]”. You can have an idea in the morning and a tangible product by the afternoon.
Unlock the potential of AI with simplified and standardized data integration through MCP
MCP is not just a protocol. This is a shift in how AI agents interact with the world. Complexity is no longer a development hurdle and most development will be done through building an AI agent, then instructing it through natural language. We already have programs writing programs. As AI gets more entwined with execution and deployment, we’ll see development move further into the realm of ideas — not just traditional computer science.
This movement isn’t going to replace developers, it will make software development accessible. In the middle ages, literacy was considered a luxury. Nowadays, development literacy is like reading. As we move into the golden age of AI, MCP will allow everyone to be literate in development. We’re at the dawn of a renaissance.