What MCP is and what it solves
MCP (Model Context Protocol) is an open standard for connecting AI applications to external systems. The analogy in the official docs is a good one: MCP is like a USB-C port for AI applications — just as USB-C standardises how devices connect, MCP standardises how AI applications connect to data sources and tools.
The problem it solves is the N×M problem. Before MCP: with 5 AI tools and 10 systems (Jira, Sentry, Firebase, PostgreSQL…) you had to write 50 separate integrations. With MCP each tool speaks MCP client and each system exposes one MCP server — 5 + 10 = 15 components.
| Without MCP | With MCP |
|---|---|
| Every tool writes a separate integration for every system | Every system exposes one MCP server, once |
| The integration is tool-specific — rewritten when the tool changes | The server works with every MCP client |
| Waiting on the tool vendor to connect your internal system | You write your own server yourself |
What it means in practice for an Android team:
- Error tracking — the agent can pull a specific crash's stack trace from Sentry/Crashlytics and go look at the code.
- Issue tracking — read a Jira/Linear ticket's description and write code against it.
- Design — take a component's sizes and colours from Figma and turn them into Compose code.
- Internal systems — your feature-flag service, your CI, your internal API docs.
The key distinction to hold on to: a skill teaches the agent how to work; MCP gives it what it has access to.
MCP is an open protocol and not specific to Claude — VS Code, Cursor and other tools support it too. That matters for a practical decision: an MCP server you write for an internal system does not depend on which tool your team picks.
📚 Sources and documentation
- What is MCP — official introductionofficialmodelcontextprotocol.io
- Claude Code — MCPofficialcode.claude.com