An Introduction to MCP, Its Usage, and Security Considerations

• MCP Components
• MCP Core: JSON-Mediated Communication
• MCP Security Considerations
• Conclusion

Model Context Protocol (MCP) is an open-source protocol that standardizes communication between AI models and external services like databases, APIs, or local tools.

In this article, I'll break down how MCP works, why it's powerful, and some of the security considerations to keep in mind.

MCP Components

The name Model Context Protocol might sound abstract, but the concept is straightforward: MCP is to AI–tool communication what HTTP/HTTPS is to web client–server interaction.

The key components are:

• MCP server: provides tools, resources, and prompts for AI models to use (the context)
• MCP client: the AI client that connects to the server and consumes those capabilities.

At the core is the data layer protocol, which defines primitivesthat let AI clients and servers describe what they can do. The main primitives are:

• Tools: functions the AI can execute to perform tasks
• Resources: data the AI can access
• Prompts: reusable templates that organize interactions with AI models

MCP Core: JSON-Mediated Communication

At its simplest, MCP is just structured messaging: JSON-RPC over a transport layer (like stdin/stdout, sockets, or HTTP).

For example, an AI client can ask a server what tools are available:

{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/list"
}

And the server might respond with:

[
  {
    "name": "docs.search",
    "description": "Search documents by keyword",
    "input_schema": {
      "type": "object",
      "properties": {
        "query": { "type": "string" }
      },
      "required": ["query"]
    }
  },
  {
    "name": "docs.get",
    "description": "Retrieve a document by ID",
    "input_schema": {
      "type": "object",
      "properties": {
        "id": { "type": "string" }
      },
      "required": ["id"]
    }
  }
]

Every interaction follows this structured JSON format. That simplicity is the source of MCP's power: Before MCP, each service integration required writing custom, one-off connectors. With MCP, however, integrations become standardized, portable, and tool-agnostic.

MCP Security Considerations

The power of MCP also introduces risk. If misconfigured, an MCP server could unintentionally expose sensitive data or system functionality to AI clients.

For instance:

• An MCP server with access to your local filesystem might let an AI read files you didn’t intend to share.
• A poorly designed tool could act as a backdoor, bypassing normal approval flows or security checks.

This is why MCP servers should be designed with least-priviledge principles: expose only the tools and resources necessary, and enforce access controls.

For more details on common threats and security best practices, check out MCP and Agentic Security Conference, 2025.

Conclusion

MCP may sound abstract at first, but once you see it in action, it's actually quite simple. Its strength lies in that simplicity: JSON-based way for AI models to talk to external systems.

That said, with greater integration capabilities come greater responsibility. Developers and organizations should adopt established cybersecurity practices when deploying MCP tools.

If you’re curious about how MCP and AI agents raise new security challenges (like prompt injection and tool poisoning), I’ve written another article summarizing insights from the MCP and Agentic Security Conference, 2025. You can read it here.