What can MCP actually do?
MCP lets an AI agent connect to your real tools and chain them together, which a plain chatbot cannot do. The value is not in any single connection but in wiring several systems into one workflow.
MCP (Model Context Protocol)AI Agents
1. Workflows across multiple systems
A chatbot answers in its own box. With MCP, an incident can fire and the agent pages a human while it simultaneously reads the codebase, finds the cause, and opens a pull request. By the time you log in, the fix is waiting for approval. That is several tools acting as one workflow, which is not possible on a closed machine.
2. Actions, not just answers
Angie Jones’s Slack example: a developer tags the agent on a suspected bug, it reads the conversation, checks the code through the GitHub connection, confirms the bug, proposes fixes, and opens the PR the team picks. Five minutes, no call, no IDE. The agent is operating tools, not describing what to do.
3. New products for your customers
Angie’s sharper point: the big use case is not your internal developer workflow. It is what you can build for your customers by connecting applications together that could not talk before. MCP is a product surface, not just an internal convenience.
The caveat
None of this means installing random connectors. At a company handling money, Block keeps an allowed list and routes every server through security first. Openness plus guardrails.
Why it matters
The dismissal of MCP as “fancy function calling” misses the point. The value shows up the moment you connect the second and third system, because that is when an agent stops answering and starts doing the work.
From the conversation
This explainer is drawn from these episodes — each carries its full transcript.
