What problem does MCP solve in agent systems?

What you need to know

The M × N problem

Imagine a company with three AI applications — an IDE assistant, a chat assistant and an internal support agent — and four systems they need: GitHub, Postgres, Jira and Google Drive. Without a standard:

• Each app writes its own GitHub integration: 3 copies, 3 sets of bugs, 3 auth flows.
• In total, 3 × 4 = 12 integrations to build and maintain.

With MCP, each system has one server and each app has one MCP client implementation: 3 + 4 = 7 pieces, and adding a fifth system is one new server that all three apps can use.

What MCP standardises

• Discovery — "what can you do?" (tools/list, resources/list, prompts/list).
• Invocation — "do this with these arguments" (tools/call, resources/read).
• Result shape — text, images, structured JSON, and a clear error flag.
• Transport and auth — stdio for local servers, Streamable HTTP with OAuth-based authorisation for remote ones.

What MCP does not do

• It does not make the model smarter or choose tools for it — the model still sees tool definitions and picks.
• It does not replace function calling. The app turns MCP tools into ordinary tool definitions for its model API.
• It does not make a server safe. A malicious or buggy server is still a risk.

A real-life example

A mid-sized software company has a GitHub triage bot, an IDE assistant for developers, and a chat assistant in Slack. All three need to read issues, search code and add labels.

Before MCP, the platform team kept three GitHub integrations. When GitHub changed a rate-limit header, two broke and one silently returned empty results for a week.

After MCP, they run one GitHub MCP server (they could also use the official one GitHub publishes). All three apps connect to it. When they add a github_list_failed_checks tool on Tuesday, all three apps can use it the same day, without a release. When the rate-limit logic changes, it is fixed in one place.

They are honest about the cost: one more service to deploy, monitor and secure, and one more network hop per call.

Follow-up questions to expect

• "Who uses MCP?" — Many AI clients and IDEs support it as clients, and many SaaS vendors publish MCP servers; model APIs such as Anthropic's can also connect to remote MCP servers directly.
• "Is MCP only for Claude?" — No. It is model-agnostic; the host converts MCP tools into whatever format its model API needs.
• "Is MCP an agent framework?" — No. It is a protocol for connecting to tools and context. The agent loop, planning and memory live in the host.