daBongo LMS AI Training Courses

Lesson Content

The integration problem before MCP.

Before a standardized protocol, connecting an AI model to external tools required a custom integration for every tool-model pair. Connecting Claude to a GitHub repository, a Postgres database, and a Slack workspace required three separate custom integrations – each maintaining its own authentication, schema, and update path. N models x M tools = NxM custom integrations.

What MCP standardizes.

MCP defines a common interface between AI models (clients) and external tools (servers). A tool built as an MCP server connects to any MCP-compliant AI client using the same protocol – once. The NxM problem becomes N+M: build one MCP server per tool, build one MCP client per model.

MCP vs. Anthropic API tool use.

The Anthropic API's tool use mechanism (function calling) is a model-level feature – your application defines tools in the API request and executes them directly. MCP is a separate server process running alongside your application, exposing capabilities to any connected client via the standard protocol.

When to use API tool use:

• Simple function calls your application already executes
• Tight integration with application logic
• No need for cross-client reuse

When to use MCP:

• Capabilities that should be reusable across multiple Claude applications or models
• Complex tool implementations that benefit from a standalone server
• Exposing organizational capabilities to multiple AI integrations

The MCP ecosystem.

MCP has a growing ecosystem of pre-built servers (file system, Git, Postgres, Slack, GitHub, and many more – verify current ecosystem at modelcontextprotocol.io). Before building a custom server, check whether a maintained pre-built server exists for your use case.