Problems MCP Model Context Protocol solves

Hey everyone! If you’ve been tinkering with AI, especially Large Language Models (LLMs), you’ve probably felt that exhilarating rush when your creation almost does something amazing. But then, reality hits. 

Getting that AI to reliably talk to other apps, access live data, or even just understand the specific context of what you’re working on can feel like wrestling an octopus. It’s a mess of custom code, brittle integrations, and a whole lot of “why isn’t this working?!”

Well, what if I told you there’s a movement to bring a little order to this chaos? Enter the Model Context Protocol (MCP). Think of it like USB-C, but for AI applications.

It’s an open standard spearheaded by Anthropic (the folks behind Claude) aiming to create a universal way for AI models to plug into the vast world of external tools, data sources, and systems.

Sounds interesting? Let’s dive into the gnarly problems MCP is here to solve and why it’s got developers and AI enthusiasts buzzing.

The “It Works on My Machine… Sometimes” Syndrome, aka Inconsistent Context

One of the biggest headaches in building robust AI applications is managing context (the specific information an AI needs to understand and respond intelligently to a request). Imagine you’re building an AI-powered customer support bot. For it to be truly helpful, it needs:

• Real-time user behavior: What has the user been clicking on? What’s in their cart?
• Historical data: What were their past orders? Any previous support tickets?
• Product information: Is the item in stock? What are its specs?

Without a standard way to feed and manage this context, developers often end up hardcoding logic for each specific data source. This creates brittle systems. Change an API endpoint for your product database? Your AI integration might just keel over. 

Trying to deploy the same AI in a slightly different environment (say, from testing with mock data to production with live user inputs)? Get ready for a world of pain.

MCP’s Solution: MCP steps in by standardizing how context is defined, passed, and validated. It can enforce schemas for input data or automate checks to ensure, for instance, that timestamps are consistent. This means your AI gets the right information, in the right format, regardless of where it’s coming from.

A coding assistant using MCP. Instead of just having general coding knowledge, it can access the specific files you have open in your IDE (Integrated Development Environment), understand your current project’s dependencies, and even look at recent changes in your Git repository. 

Tools like Zed and Replit are already leveraging MCP to provide this kind of deeply contextual coding help.

The N×M Nightmare – Fragmented Integration Workflows

Before MCP, if you wanted your AI model to interact with, say, three different data sources (your CRM, a knowledge base, and a ticketing system) and you had two different AI models you were experimenting with, you might have to build 3 × 2 = 6 custom integrations! 

This is what’s often called the “N×M data integration problem.” Each connection is a bespoke piece of code, requiring time to build, test, and maintain. It’s a scalability nightmare.

MCP’s Solution: MCP acts as a universal adapter. Instead of building point-to-point integrations, you build an MCP “server” for your data source or tool. Once that server exists, any MCP-compatible AI client (your LLM application) can talk to it using a standardized language. This drastically reduces the development overhead. Update your CRM’s API? You only need to update the MCP server for that CRM, not every single AI application that uses it.

Real-World Example: Imagine an enterprise AI assistant designed to help employees. This assistant might need to access information from Confluence (for documentation), Jira (for project status), and Salesforce (for customer data). With MCP, each of these services can have an MCP server. The assistant can then query these servers in a standard way to gather information and provide comprehensive answers, without developers having to write unique glue code for each one. Companies like HackerOne are using MCP to connect their AI agents to internal systems securely.

Lack of Standardized Communication

When different teams (data scientists, backend engineers, MLOps specialists) work on an AI project, miscommunications about how context should be handled can lead to significant delays. A data scientist might assume a model receives perfectly preprocessed location data as a geohash string, while the backend team sends raw GPS coordinates. This mismatch can lead to subtle bugs that are hard to track down.

MCP’s Solution: MCP establishes a common, machine-readable language for these interactions. It clearly defines how requests and responses should be structured, what capabilities a tool server offers (like reading a file, executing a function, or fetching data), and what parameters are expected. This shared understanding, embedded in the protocol itself, minimizes ambiguity and ensures everyone is on the same page.

Real-World Example: Consider a multi-tool AI agent that first needs to look up a document in a vector database and then, based on the findings, draft an email and send it via a messaging API. MCP can define the sequence, the data format passed between these steps, and how errors are handled, ensuring a smooth flow across these different tools.

Giving AI “Hands and Eyes”: Accessing Real-Time and Sensitive Data Securely

For AI to move beyond being a sophisticated parrot of its training data, it needs to interact with the world in real-time and access current, specific, and often sensitive information. Think about an AI financial advisor needing access to live stock prices or an AI doctor’s assistant needing to pull up (with permission, of course!) a patient’s latest lab results.

MCP’s Solution: MCP is designed with this in mind. It facilitates secure, two-way connections. A key aspect is host-mediated security. The “host” application (like your desktop or a specific AI-powered app) can manage permissions, ensuring that the AI model only accesses what it’s explicitly allowed to.

For instance, the Claude Desktop app uses MCP to allow the AI to read local files on your computer, but it does so in a way that keeps your data on your device unless you explicitly consent to share it.

Real-World Example: A sales team using an AI assistant integrated with their CRM (Customer Relationship Management) system via MCP. The assistant can pull live updates on leads, log meeting notes directly into the CRM, or even schedule follow-ups, all while adhering to the access permissions set within the CRM and managed by the MCP host.

Apollo.io, a sales intelligence platform, is an example of a company using MCP to connect assistants to such business systems.

MCP Configs (A Glimpse)

So, how does this look in practice? While deep-diving into code is beyond a single blog post, let’s look at a conceptual example of how you might configure an MCP server in a project. Often, this involves a JSON configuration file.

For example, if you were connecting an AI development environment like Cursor or VS Code with Copilot to a Supabase (a backend-as-a-service platform) MCP server, your project might have a .cursor/mcp.json or .vscode/mcp.json file.

A simplified snippet for enabling a Supabase MCP server might look something like this (actual configurations can vary based on the tool and server):

{
  "mcpServers": {
    "supabase": {
      "command": "npx", // Command to run the server
      "args": [         // Arguments for the command
        "-y",
        "@supabase/mcp-server-supabase@latest", // The Supabase MCP server package
        "--access-token",
        "<YOUR_SUPABASE_ACCESS_TOKEN>" // Your specific access token
      ]
    }
  }
}

Or, for VS Code where you might be prompted for sensitive info:

{
  "inputs": [
    {
      "type": "promptString",
      "id": "supabase-access-token",
      "description": "Supabase personal access token",
      "password": true
    }
  ],
  "servers": {
    "supabase": {
      "command": "npx",
      "args": ["-y", "@supabase/mcp-server-supabase@latest"],
      "env": {
        "SUPABASE_ACCESS_TOKEN": "${input:supabase-access-token}" // Uses the prompted input
      }
    }
  }
}

(You can find more detailed examples in the Supabase MCP documentation)

This tells your AI environment: “Hey, there’s an MCP server for Supabase. Here’s how to start it and authenticate.” Once active, your AI can then leverage the “tools” and “resources” exposed by that Supabase server (e.g., to query your database tables).

Why This Matters for MLOps (Machine Learning Operations)

MCP isn’t just a developer convenience; it has significant implications for the broader MLOps ecosystem:

• Reproducibility: By standardizing how context is provided, it’s easier to reproduce AI behavior across different environments.
• Standardization & Collaboration: Teams can build and share MCP servers for common tools and data sources, fostering an ecosystem of reusable components.
• Scalability: Easier to integrate new tools and data sources as your AI applications grow more complex.
• Modularity: AI models, tools, and data sources can evolve independently as long as they adhere to the MCP interface.
• Simplified Management: Reduces the complexity of managing countless custom integrations.

An Open Standard for a Connected AI Future

MCP is still relatively new, having been introduced by Anthropic in late 2024, but it’s rapidly gaining traction. Major players like Google DeepMind and OpenAI have signaled support or are exploring its use. 

An ecosystem of open-source MCP servers is already growing for various applications like GitHub, Slack, Docker, and more. You can explore the protocol specifications and SDKs (Software Development Kits) in multiple languages on the Model Context Protocol GitHub organization.

The dream is a future where connecting an AI to a new tool or data source is as simple as plugging in a USB device. No more custom wiring, no more N×M headaches. Just seamless, contextual, and powerful AI.

While there are still challenges to address, including evolving security considerations, MCP represents a significant step towards a more interoperable and capable AI ecosystem. It’s definitely a space to watch!

What are your thoughts? Have you encountered these integration challenges? Let me know in the comments below!

Priyansh Khodiyar

Priyansh is Developer Relations Advocate who loves technology, writer about them, creates deeply researched content about them.