OpenAPI vs MCP: choosing the right integration format for AI tools

If you're building a service that AI assistants can use, you currently have two main integration targets: OpenAPI specs for ChatGPT Custom GPT Actions, and MCP servers for Claude and the growing ecosystem of MCP-compatible agents. ReminderIt ships both. Here's how the two formats compare.

OpenAPI: mature, REST-native, ChatGPT-focused

OpenAPI (and its predecessor Swagger) is a well-established format for describing REST APIs. ChatGPT Custom GPT Actions import an OpenAPI 3.1 spec by URL. Authentication is straightforward — API key in a header. If your service already has a REST API and a Swagger/OpenAPI spec, adding ChatGPT support is minimal extra work.

MCP: JSON-RPC, tool-centric, and growing fast

MCP defines tools (functions with names, descriptions, and typed parameters) rather than HTTP endpoints. This makes it more natural for AI models to reason about — 'call the create_reminder tool' rather than 'POST to /reminders'. MCP also supports streaming responses and stateful sessions, which OpenAPI doesn't handle natively.

Which to implement first

If your primary user base is on Claude (Desktop or web), implement MCP first — it's the richer integration and Claude's tool-use is strongest with MCP. If you have existing ChatGPT users or a large OpenAPI spec, start there. Ideally, implement both: they're not mutually exclusive and reach different audiences.

ReminderIt's approach

ReminderIt ships an MCP server (pip install reminderit-mcp) for Claude and an OpenAPI spec (chatgpt-action/openapi.yaml in the GitHub repo) for ChatGPT. The underlying REST API is the same; only the interface layer differs. This two-track approach means users choose their preferred AI assistant and both get the same phone call delivery.