One of the challenges with using AI and LLMs to generate code today is that they mostly produce generic code. That shouldn’t surprise us.

These systems are probabilistic word generators (sorry, no offense to my future AI overlords) trained on a vast ocean of open source projects. What you get is the statistical average of that code — competent, but rarely inspired.

But Model Context Protocol (MCP) changes that game entirely. And if history is any guide, the shift could rival the biggest turning points in how software gets built.

First, What MCP Is (in Plain Terms)

MCP is a way for tools to advertise specialized capabilities to an AI model via APIs.

Instead of treating the LLM as a closed-text generator, MCP lets external systems expose functions (“capabilities”) the model can call — securely, with schemas and policies — so generation and validation can be composed.

Think of it like this:

1. Capability registration: Tools (SCA scanners, SAST engines, test runners, refactoring services, linters, build systems, ticketing, etc.) publish what they can do — functions, input/output schemas, and constraints.

2. Discovery and selection: The AI sees an indexed catalog of capabilities relevant to the user’s task, along with usage affordances (parameters, costs, scopes).

3. Policy and permissions: Calls are gated by organizational policies (who can call what, with which data), environment scopes, and auditing. Sensitive actions require elevated consent.

4. Invocation and streaming: The AI composes calls (often in parallel), streams intermediate results, and uses outputs to steer the next step (e.g., “SAST flagged X, propose refactor, rerun tests”).

5. Observability and feedback: Every call is recorded. Results (pass/fail, severity, timing) feed back into prompts and org analytics for continuous improvement.

6. Decoupled runtime: Tools can run anywhere (local, VPC, SaaS). MCP is the contract between the AI and your (Read more...)