> For the complete documentation index, see [llms.txt](https://docs.launchboard.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.launchboard.org/quick-projects/ai-chats-with-mcp+sqlite.md). # AI Chats with MCP+SQLite ## Introduction {% embed url="" %} **What is this project about?**\ You will be guided on how to quickly offer your own custom abilities to an LLM (Large Language Model) chat, like Claude.ai, without needing to train your own LLM or create a chat interface. You will do this by creating an MCP server (Model Context Protocol server) that has quickly become an industry standard. You will also be shown how to create and interact with a SQLite database within your MCP server to offer custom data to chat users. **What tools will we use?** * Github for managing your server code in a repository * Python programming in Github Codespaces or similar development environment * FastMCP Cloud for running your server for public access Next, take a look at the [quick start with template](/quick-projects/ai-chats-with-mcp+sqlite/quick-start-with-template.md). --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.launchboard.org/quick-projects/ai-chats-with-mcp+sqlite.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.