What Is MCP (Model Context Protocol)? A Practical Guide

The N×M integration problem

Before MCP, AI tool integrations were fragmented. If you built an agent that worked with Jira, then wanted it to read Google Drive or Notion, you usually had to build separate integrations for each one.

The same problem existed on the other side too. If a platform wanted multiple models to access its data, it often had to build provider-specific integrations for each model stack.

That is the N×M integration problem: N models multiplied by M tools and data sources leads to too many one-off integrations.

What MCP is

The Model Context Protocol (MCP) was introduced by Anthropic as an open standard for connecting AI systems to tools and context. The common shorthand is that MCP is like "USB-C for AI": one protocol that can connect many clients to many resources.

Instead of writing custom connectors for every model and every data source, developers now build to the MCP standard.

1. MCP Servers: Lightweight programs exposing specific data sources (like your PostgreSQL database) or tools (like an internal corporate search engine) using the standard MCP format.
2. MCP Clients: Any AI agent, LLM application, or IDE (like Claude for Desktop, OpenClaw, or Visual Studio Code) that knows how to speak the MCP protocol.

When you connect an MCP client to an MCP server, the model can discover available tools and use them through a standardized message format.

Why MCP Matters for Enterprise Security

One of the biggest hesitations enterprises have about autonomous AI agents is the risk of data exfiltration. If an AI agent has the keys to your entire GitHub repository and your billing database, what happens if it is tricked by a malicious prompt injection?

MCP can support stronger security and governance when you deploy it carefully:

• Granular Permissions: MCP servers are intentionally narrow. An MCP server for GitHub can be configured to only allow "read-only" operations on specific repositories, preventing the AI from accidentally deleting production code.
• Separation of Concerns: The model client does not need to hold every downstream credential directly. The MCP server can hold the service credentials and expose only the allowed interface.
• Local Sandboxing: Because MCP commonly runs over standard input/output or local HTTP, teams can keep servers inside private or isolated environments.

Running MCP on private infrastructure

MCP fits naturally with GetClaw's AI Gateway and other private agent infrastructure.

If your team deploys a GetClaw VPS, you can run MCP servers on the same private host as your gateway and related tooling.

# Example: Deploying an MCP Server on a GetClaw Node
mcp_servers:
  postgres_internal:
    command: "npx"
    args: ["-y", "@modelcontextprotocol/server-postgres", "postgresql://admin:password@localhost/enterprise_db"]
  slack_bot:
    command: "npx"
    args: ["-y", "@modelcontextprotocol/server-slack"]

Because everything stays inside the same private environment, the gateway can reach those MCP servers without exposing the backing services directly to the public internet.

Why MCP matters now

MCP has become one of the clearest emerging standards for tool access in AI systems.

For teams building agents, the value is straightforward: fewer one-off integrations, cleaner tool boundaries, and a more portable way to connect models to real systems.

FAQ

What problem does MCP solve?

It solves the connector sprawl between many models and many tools or data sources by standardizing the interface.

Is MCP only for Anthropic tools?

No. MCP is an open protocol and is now discussed and adopted across a wider AI tooling ecosystem.

Sources and notes

• Anthropic defines MCP as an open protocol for standardized access to tools and context.
• Related reading: MCP security in 2026, self-hosted AI agents.