Usman Arif
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1 July 2026 · 7 min

MCP: Giving Your LLM Hands (Carefully)

#mcp#ai-agents#tools
TL;DR

MCP is a standard way for models to discover and call tools and data sources — USB-C for LLM tools. Convenient, but every connected server is new attack surface, so treat tool output as untrusted.

For a while, wiring a model up to your tools meant hand-rolling glue for every integration. The Model Context Protocol standardised that handshake: a common way for a model to discover what tools and data a server offers, and to call them. People describe it as USB-C for LLM tools, and that's a fair summary — one connector, many devices.

Why it caught on

Standards win when they kill boilerplate, and MCP kills a lot of it. Instead of N models times M tools worth of bespoke adapters, a tool exposes an MCP server once and any MCP-aware client can use it. That's how you get an ecosystem instead of a pile of one-off scripts.

The part nobody puts on the slide

Every tool you connect is also attack surface. Tool descriptions and tool results are text that flows into your model's context — which means a malicious or compromised source can attempt prompt injection through them. 'The model read a web page and then emailed your contacts' is a real failure mode, not a hypothetical.

Defend accordingly: least-privilege scopes on every server, human confirmation for anything irreversible, and a healthy distrust of any content the model didn't get from you.

The takeaway

MCP is genuinely good infrastructure and worth adopting. Just remember that giving a model hands is exactly as powerful — and as dangerous — as it sounds. Connect deliberately, scope tightly, and log everything.

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