Sparround

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 MCPWith MCP
Every tool writes a separate integration for every systemEvery system exposes one MCP server, once
The integration is tool-specific — rewritten when the tool changesThe server works with every MCP client
Waiting on the tool vendor to connect your internal systemYou 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