What can MCP actually do?
MCP lets an AI agent connect to your real tools and chain them together, which is the thing a plain chatbot can't do. The value isn't in any single connection — it's in wiring several systems into one workflow. 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's when an agent stops answering and starts doing the work.
MCP (Model Context Protocol)AI Agents
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’s several tools acting as one workflow, which isn’t possible on a closed machine.
Actions, not just answers
A developer tags the agent on a suspected bug in Slack; it reads the conversation, checks the code through a 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.
New products for your customers
The sharper point: the big use case isn’t your internal developer workflow. It’s what you can build for your customers by connecting applications that couldn’t talk to each other before. MCP is a product surface, not just an internal convenience.
The caveat
None of this means installing random connectors. A company handling money keeps an allowed list and routes every server through security first. Openness plus guardrails.
Why it matters
The value of MCP is invisible on the first connection and obvious on the third. Connect enough systems and an agent stops answering questions and starts completing work end to end.
From the conversation
This explainer is drawn from these episodes — each carries its full transcript.
