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The Model Context Protocol (MCP) has become one of the most talked-about developments in AI integration since its introduction by Anthropic in late 2024. If you’re tuned into the AI space at all, you’ve likely been inundated with developer “hot takes” on the topic. Some think it’s the best thing ever; others are quick to point out its shortcomings. In reality, there’s some truth to both.
One pattern I’ve noticed with MCP adoption is that skepticism typically gives way to recognition: This protocol solves genuine architectural problems that other approaches don’t. I’ve gathered a list of questions below that reflect the conversations I’ve had with fellow builders who are considering bringing MCP to production environments.
1. Why should I use MCP over other alternatives?
Of course, most developers considering MCP are already familiar with implementations like OpenAI’s custom GPTs, vanilla function calling, Responses API with function calling, and hardcoded connections to services like Google Drive. The question isn’t really whether MCP fully replaces these approaches — under the hood, you could absolutely use the Responses API with function calling that still connects to MCP. What matters here is the resulting stack.
Despite all the hype about MCP, here’s the straight truth: It’s not a massive technical leap. MCP essentially “wraps” existing APIs in a way that’s understandable to large language models (LLMs). Sure, a lot of services already have an OpenAPI spec that models can use. For small or personal projects, the objection that MCP “isn’t that big a deal” is pretty fair.
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The practical benefit becomes obvious when you’re building something like an analysis tool that needs to connect to data sources across multiple ecosystems. Without MCP, you’re required to write custom integrations for each data source and each LLM you want to support. With …