The Rise of MCP Servers: A New Interface Between AI and Tools

The Model Context Protocol (MCP) is emerging as one of the most important technologies in the evolving world of AI-assisted workflows. If you've ever wished your AI assistant could directly access your files, run commands, or pull structured data from APIs safely, then MCP is the bridge that makes this possible.

What is MCP?

MCP (Model Context Protocol) is a universal standard for defining tool servers that an AI model can interact with. Instead of a model being isolated and “guessing” what to do, MCP allows it to:

• Fetch real data
• Interact with external systems
• Perform operations with context
• Respect permission and safety boundaries

In simple terms:

MCP turns AI models from “smart typists” into real, context-aware digital operators.

Key Components of MCP

MCP servers declare capabilities using a standardized JSON schema. The model can then call these capabilities as tools, just like function calls.

Why MCP Matters

Traditionally, AI models were isolated. They responded to prompts based purely on training data. But in real work environments, we need models to:

• Read and modify files
• Query databases
• Call APIs
• Automate workflows
• Execute reasoning steps over real-world context

MCP breaks down the wall between AI and “the system,” while keeping everything transparent and permission-based.

This is huge for:

• Developers
• Automation workflows
• Research tools
• Code intelligence
• Enterprise data access

Setting Up a Simple MCP Server

Let’s connect to a GitHub MCP server, which allows a model to interact with GitHub repositories.

1. Install the MCP CLI

npm install -g @modelcontextprotocol/cli

2. Install the GitHub MCP Server

npm install -g @modelcontextprotocol/server-github

3. Create a Configuration File

Create mcp.json:

{
  "clients": {
    "chat": {
      "servers": {
        "github": {
          "command": "mcp-server-github",
          "args": ["--token", "$GITHUB_TOKEN"]
        }
      }
    }
  }
}

Set your GitHub personal access token:

export GITHUB_TOKEN="YOUR_PERSONAL_ACCESS_TOKEN"

4. Test Connection

mcp interactive github

If successful, you’ll now have access to MCP tools such as:

listRepos
readFile
searchCode
createIssue

Example MCP Tool Call (via JSON-RPC)

{
  "method": "call_tool",
  "params": {
    "tool": "readFile",
    "arguments": {
      "repo": "octocat/Hello-World",
      "path": "README.md"
    }
  }
}

Practical Example: Letting an AI Modify a README

Once integrated into a chat model supporting MCP, you could simply say:

Update the README in octocat/Hello-World to include installation instructions.

The model will:

1. Read the file with readFile
2. Produce a modified version
3. Use createCommit (or PR) to apply the update

No guessing. No hallucination. Real, applied action.

The Future of MCP

MCP signals a shift in how we use AI:

We are moving toward AI as a universal operator interface:

• AI agents maintaining codebases
• AI onboarding new developers
• AI running research experiments
• AI managing infrastructure ops
• AI controlling real-time business workflows

MCP becomes the “shared language” for tools and models — the missing glue layer.

Expect to See:

• IDEs with built-in MCP toolchains
• Enterprise workflows driven by AI operators
• AI assistants with real-time state awareness
• New standards for safe & controlled capability access

Final Thoughts

MCP is more than a protocol — it’s a new mental model for how we collaborate with AI.

It turns AI from “assistant that talks” into “assistant that acts.”

If the web connected computers to each other, and APIs connected services to apps, then MCP is connecting AI to the real world.

The implications are massive — and we are just at the beginning.