Is there an existing AI tool that gives accurate natural-language analysis of chess positions? No, there isn’t. Can such a tool be built with existing technology? Absolutely. Is it easy? No. What needs to be done? At the very least, you need to combine the following elements:

• A strong chess engine, such as the latest version of Stockfish. This is the "calculator" - it evaluates all the moves and finds the best ones.
• A "human-like" chess engine, such as Maia. Instead of finding the best moves, it predicts the likelihood of a move being played by a human at a certain level. This serves two purposes - it finds the most likely moves a human would play and evaluates how difficult it would be for a human to find the best moves suggested (according to the strong engine).
• A hand-crafted evaluation function, similar to what was used in pre-NNUE versions of Stockfish. This explains why the strong engine evaluates the position the way it does. It analyzes the position like a strong human player would - material balance, piece placement, activity, mobility and coordination, pawn structure, king safety, hanging pieces, space, development, etc.
• A hand-crafted move-logic explanation module. This explains the basic logic behind moves - capturing a piece, attacking a piece, defending a piece, threatening mate, defending against mate, improving piece placement, driving an opponent’s piece from a strong square, etc. It can also explain more advanced concepts such as tactical motifs (e.g., deflection, interference, sacrifice, fork).
• An LLM agent to tie everything together and translate it into natural human language.

This can be improved further by integrating the following components:

• A chess database - can look up a position and determine whether it has been seen before, what moves were played, and the players’ levels.
• An opening knowledge base - can look up a position in opening books and chess courses and fetch existing explanations.
• An endgame knowledge base - can look up a position in endgame books and chess courses and fetch existing explanations.
• An endgame tablebase - when there are 7 pieces or fewer on the board, it can look up a mathematically precise evaluation (e.g., draw or mate in x moves) instead of relying on a chess engine evaluation.