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Has anybody already implemented a MCP https://modelcontextprotocol.io/introduction Server in Delphi? Any tips, or quickstarts?

Hi everyone, (Oops, I made a mistake posting in the VCL section when it's more appropriate here) For more than six months, I’ve been developing turnkey solutions for the Delphi developer community in the form of API wrappers that harness the full range of models and features offered by leading providers (OpenAI, Deepseek, Anthropic, Gemini, MistralAI, Groq Cloud, Hugging Face, and Stability AI). Each README file is written as a tutorial to help users get started. Natively, these libraries support both synchronous and asynchronous workflows and some even offer parallel execution to optimize performance. In addition, I’ve published two projects demonstrating the use of the Promise pattern to orchestrate asynchronous requests (notably with OpenAI, but also compatible with other wrappers), as well as a pipeline-based project that simplifies the processing chain for model calls. Coming soon, I will introduce a module dedicated to OpenAI’s new file_search feature (endpoint v1/response), which, thanks to an expanded semantic surface, enables querying vector stores with unprecedented precision and temporarily enriches models without the need for fine-tuning. If you’re new to AI integration or looking to accelerate your development, feel free to check out my projects on GitHub. Additionally, all of these tools are available via GetIt. Your feedback is welcome and will be incredibly helpful in improving these tools. https://github.com/MaxiDonkey As a point of clarification regarding my background, I am a mathematician by training rather than a professional software developer. I returned to Delphi just over a year ago—if you’d like to understand my motivations, please consult this document. My core expertise lies in using formal proof tools such as Lean and Coq, rather than in Delphi-specific development.

Hi everyone, For more than six months, I’ve been developing turnkey solutions for the Delphi developer community in the form of API wrappers that harness the full range of models and features offered by leading providers (OpenAI, Deepseek, Anthropic, Gemini, MistralAI, Groq Cloud, Hugging Face, and Stability AI). Each of these libraries supports both synchronous and asynchronous workflows, and some even offer parallel execution to optimize performance. In addition, I’ve published two projects demonstrating the use of the Promise pattern to orchestrate asynchronous requests (notably with OpenAI, but also compatible with other wrappers), as well as a pipeline-based project that simplifies the processing chain for model calls. Coming soon, I will introduce a module dedicated to OpenAI’s new file_search feature (endpoint v1/response), which, thanks to an expanded semantic surface, enables querying vector stores with unprecedented precision and temporarily enriches models without the need for fine-tuning. f you’re new to AI integration or looking to accelerate your development, feel free to check out my projects on GitHub. Please note that I only provide the entry point to my GitHub page, from which you can navigate to the repositories that interest you. Additionally, all of these tools are available via GetIt. If you’d like me to share direct links to each repository, I can do so in this thread. Your feedback is welcome and will be incredibly helpful in improving these tools. https://github.com/MaxiDonkey

The forthcoming version of PyScripter will have built-in integration with LLM. See the blog post for a taste of the functionality provided.