What if the biggest problem with AI coding agents isn't that they don't have enough tools—but that they have too many?

An agent with access to GitHub, CI/CD, databases, documentation, issue trackers and deployment infrastructure sounds powerful.

But every additional capability introduces another question:

Should the agent use it?

And then:

Does it have the right context?
Does it have permission?
Is this the right workflow?
How do we verify the result?

This is where MCP plugins move beyond simple tool integration.

The interesting architectural question isn't:

“How many tools can my AI agent access?”

It's:

“How intelligently can my agent compose the capabilities it needs to achieve a specific goal?”

In the article, I examine an architecture built around:

Intent → Workflow → Capabilities → Context → Action → Verification

and explore why MCP may increasingly become the infrastructure layer underneath reliable AI developer workflows.

If you're building or evaluating AI coding agents, MCP integrations, or agentic developer systems, this is the part of the MCP conversation worth watching.

👉 Read the full article:https://neuralcoretech.com/mcp-plugins-developer-workflows/

Question: Which action would you allow an AI coding agent to perform autonomously—and which would always require human approval?