What is AI MCP? Model Context Protocol Explained

What Is AI MCP?

AI MCP stands for Model Context Protocol. Anthropic introduced MCP in November 2024 as an open standard for integrating AI with external tools, databases, and applications.

MCP solves a core limitation in AI systems. Large language models require live data and specialized capabilities. Before MCP, developers built custom integrations for every tool. MCP provides a universal, cross-platform standard for connecting AI models to external resources.

How AI MCP Works

MCP follows a simple architecture made of three components:

1. MCP Servers

• Expose tools, functions, and data sources
• Declare available capabilities through a standard schema
• Run as standalone processes that handle specific tasks
  

2. MCP Clients

• Bridge AI models to MCP servers
• Receive tool calls from the model
• Route requests to the correct server
• Support multiple server connections at the same time
  

3. MCP Hosts

• Provide the runtime environment
• Run locally or remotely
• Coordinate communication between clients and servers
  

Workflow Example

1. You send a prompt to an MCP-enabled AI model.
2. The model determines which tools it needs.
3. The MCP client forwards the call to the proper server.
4. The server executes the operation and returns results.
5. The model uses the returned data to generate its final response.
   

All communication uses JSON-RPC 2.0, a lightweight message format that keeps responses fast and standardized. Any MCP-compliant server can communicate with any MCP-compliant client.

What Is MCP Used for in AI Applications?

MCP shifts AI from static, fixed training data toward dynamic, real-time capabilities. MCP-enabled AI models can:

• Query live databases
• Read and write files
• Interact with APIs
• Run code and access development tools
• Execute system-level operations with controlled permissions
  

Each server implements its own authentication and access control. Sensitive resources remain protected while staying accessible to trusted models.

Which AI Systems Support MCP?

Claude

• Claude Desktop includes a built-in MCP client
• Claude API exposes MCP capabilities to developers
  

Other Platforms

• Multiple AI vendors are adopting MCP
• Any model can support MCP by adding a client layer
• OpenAI, Google, and others are actively evaluating integration

Because MCP is model-agnostic, support depends only on platform developers implementing the client spec.

Who Created AI MCP?

Anthropic created MCP, released it as open source, and maintains the official protocol. The ecosystem grows through community-built servers, clients, and tools. Developers contribute through the public MCP repositories on GitHub.

MCP for Infrastructure Management

The noBGP MCP server applies the Model Context Protocol directly to cloud and edge infrastructure. Connected AI models can:

• Provision compute nodes
• Deploy applications
• Configure services
• Generate secure access URLs
  

All through natural language instructions.

Why It Matters

Traditional infrastructure deployment requires deep networking knowledge. You configure IP addresses, firewall rules, routing, load balancers, and VPNs. Each step introduces risk and complexity.

noBGP MCP removes those barriers.

You tell the AI model what you want to deploy. The model uses the noBGP platform to:

• Provision new nodes
• Install dependencies
• Configure the application
• Generate secure private or public URLs
• Deliver end-to-end encrypted connectivity
  

No public IPs.

No manual firewall rules.

No VPNs.

No networking setup.

The noBGP agent manages connectivity while the AI model manages deployment and application logic.

The Impact of MCP

MCP changes how developers build and operate systems. Instead of wrestling with infrastructure, you describe goals in plain language. The model translates your description into a running environment.

Developers focus on creating applications. AI handles the orchestration, networking, and deployment.

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