Personally I don't get why they didn't extend the function calling system [1]. This enables practically the same functionality, only with less fuzz. Long term

Personally I don't get why they didn't extend the function calling system [1]. This enables practically the same functionality, only with less fuzz. Long term memory can then instead be implemented via RAG, exposed as function calls, instead of keeping it in the context of the MCP. An "agent" is a pre-prompted server which receives external requests, by any API (the AI interface is not exposed, since there's no need for it to be). The server then performs query by announcing which tools the LLM should use via function calling + conversation flow. The only downside of this approach is that you can't have a MCP "marketplace" (but it's perfectly possible to expose standardized structs for different tools [2], which ultimately achieves the same thing). [1]: https://platform.openai.com/docs/guides/function-calling?api-mode=responses https://platform.openai.com/docs/guides/function-calling?api... [2]: https://github.com/baalimago/clai/blob/main/internal/tools/bash_tool_cat.go#L10 https://github.com/baalimago/clai/blob/main/internal/tools/b...