diff --git a/src/hooks/useAnalysisController/useBoardDescription.ts b/src/hooks/useAnalysisController/useBoardDescription.ts
index d56e1238..7fcdf6c1 100644
--- a/src/hooks/useAnalysisController/useBoardDescription.ts
+++ b/src/hooks/useAnalysisController/useBoardDescription.ts
@@ -6,6 +6,7 @@ import {
   StockfishEvaluation,
 } from 'src/types'
 import { MAIA_MODELS } from './constants'
+import { describePosition } from './useDescriptionGenerator'
 
 type ColorSanMapping = {
   [move: string]: {
@@ -33,205 +34,29 @@ export const useBoardDescription = (
       return ''
     }
 
-    const isBlackTurn = currentNode.turn === 'b'
-    const playerColor = isBlackTurn ? 'Black' : 'White'
-    const opponent = isBlackTurn ? 'White' : 'Black'
-    const stockfish = moveEvaluation.stockfish
-    const maia = moveEvaluation.maia
-    const topMaiaMove = Object.entries(maia.policy).sort(
-      (a, b) => b[1] - a[1],
-    )[0]
-
-    const topStockfishMoves = Object.entries(stockfish.cp_vec)
-      .sort((a, b) => (isBlackTurn ? a[1] - b[1] : b[1] - a[1]))
-      .slice(0, 3)
-
-    const cp = stockfish.model_optimal_cp
-    const absCP = Math.abs(cp)
-    const cpAdvantage = cp > 0 ? 'White' : cp < 0 ? 'Black' : 'Neither player'
-    const topStockfishMove = topStockfishMoves[0]
-
-    // Calculate winrate for more nuanced description (using centipawn to approximate winrate)
-    // Formula approximates winrate from CP value: 1/(1+10^(-cp/400))
-    const rawWinrate = 1 / (1 + Math.pow(10, -cp / 400))
-    const winrate = Math.max(0.01, Math.min(0.99, rawWinrate)) // Clamp between 1% and 99%
-    const toMoveWinrate = isBlackTurn ? 1 - winrate : winrate
-    const toMoveAdvantage = toMoveWinrate > 0.5
-
-    // Check if top Maia move matches top Stockfish move
-    const maiaMatchesStockfish = topMaiaMove[0] === topStockfishMove[0]
-
-    // Get top few Maia moves and their cumulative probability
-    const top3MaiaMoves = Object.entries(maia.policy)
-      .sort((a, b) => b[1] - a[1])
-      .slice(0, 3)
-    const top3MaiaProbability =
-      top3MaiaMoves.reduce((sum, [_, prob]) => sum + prob, 0) * 100
-
-    // Get second best moves to analyze move clarity
-    const secondBestMaiaMove = top3MaiaMoves[1]
-    const secondBestMaiaProbability = secondBestMaiaMove
-      ? secondBestMaiaMove[1] * 100
-      : 0
-
-    // Calculate spread between first and second-best moves
-    const probabilitySpread = topMaiaMove[1] * 100 - secondBestMaiaProbability
-
-    // Get move classifications
-    const blunderProbability = blunderMeter.blunderMoves.probability
-    const okProbability = blunderMeter.okMoves.probability
-    const goodProbability = blunderMeter.goodMoves.probability
-
-    // Check for patterns in stockfish evaluation
-    const stockfishTop3Spread =
-      topStockfishMoves.length > 2
-        ? Math.abs(topStockfishMoves[0][1] - topStockfishMoves[2][1])
-        : 0
-
-    // Get move spreads to detect sharp positions
-    const moveCpSpread = Object.values(stockfish.cp_relative_vec).reduce(
-      (maxDiff, cp, _, arr) => {
-        const min = Math.min(...arr)
-        const max = Math.max(...arr)
-        return Math.max(maxDiff, max - min)
-      },
-      0,
-    )
-
-    // Calculate position complexity based on distribution of move quality
-    const isPositionComplicated =
-      (blunderProbability > 30 && okProbability > 20 && goodProbability < 50) ||
-      moveCpSpread > 300 ||
-      stockfishTop3Spread > 100
-
-    // Check for tactical position
-    const isTacticalPosition = moveCpSpread > 500 || stockfishTop3Spread > 150
-
-    // Check if there's a clear best move
-    const topMaiaProbability = topMaiaMove[1] * 100
-    const isClearBestMove = topMaiaProbability > 70 || probabilitySpread > 40
-
-    // Check if there are multiple equally good moves
-    const hasMultipleGoodMoves =
-      top3MaiaProbability > 75 && topMaiaProbability < 50
-
-    // Calculate agreement between Maia rating levels
-    const maiaModelsAgree = Object.entries(currentNode.analysis.maia || {})
-      .filter(([key]) => MAIA_MODELS.includes(key))
-      .every(([_, evaluation]) => {
-        const topMove = Object.entries(evaluation.policy).sort(
-          (a, b) => b[1] - a[1],
-        )[0]
-        return topMove && topMove[0] === topMaiaMove[0]
-      })
-
-    // Check if evaluation is decisive
-    const isDecisiveAdvantage = absCP > 300
-    const isOverwhelming = absCP > 800
-
-    // Check for high blunder probability
-    const isBlunderProne = blunderProbability > 50
-    const isVeryBlunderProne = blunderProbability > 70
-
-    // Check if there's forced play
-    const isForcedPlay = topMaiaProbability > 85 && maiaMatchesStockfish
-
-    // Check if position is balanced but with complexity
-    const isBalancedButComplex = absCP < 50 && isPositionComplicated
-
-    // Generate descriptions
-    let evaluation = ''
-    let suggestion = ''
-
-    // Evaluation description that considers whose turn it is
-    if (isOverwhelming) {
-      if (cpAdvantage === playerColor) {
-        evaluation = `${playerColor} has a completely winning position with a ${Math.round(toMoveWinrate * 100)}% win probability.`
-      } else {
-        evaluation = `${playerColor} faces a nearly lost position with only a ${Math.round(toMoveWinrate * 100)}% win probability.`
-      }
-    } else if (cp === 0) {
-      evaluation = isBalancedButComplex
-        ? 'The position is balanced but filled with complications.'
-        : 'The position is completely equal.'
-    } else if (absCP < 30) {
-      evaluation = `The evaluation is almost perfectly balanced with only the slightest edge ${cpAdvantage === playerColor ? 'for' : 'against'} ${playerColor}.`
-    } else if (absCP < 80) {
-      if (cpAdvantage === playerColor) {
-        evaluation = `${playerColor} has a slight but tangible advantage with a win probability of ${Math.round(toMoveWinrate * 100)}%.`
-      } else {
-        evaluation = `${playerColor} faces a slight disadvantage with a win probability of ${Math.round(toMoveWinrate * 100)}%.`
+    const fen = currentNode.fen
+    const whiteToMove = currentNode.turn === 'w'
+
+    const stockfishEvals = moveEvaluation.stockfish.cp_vec
+    const maiaEvals: Record<string, number[]> = {}
+    const allMaiaAnalysis = currentNode.analysis.maia || {}
+
+    Object.keys(moveEvaluation.maia.policy).forEach((move) => {
+      maiaEvals[move] = new Array(MAIA_MODELS.length).fill(0)
+    })
+
+    MAIA_MODELS.forEach((model, index) => {
+      const modelAnalysis = allMaiaAnalysis[model]
+      if (modelAnalysis?.policy) {
+        Object.entries(modelAnalysis.policy).forEach(([move, probability]) => {
+          if (!maiaEvals[move]) {
+            maiaEvals[move] = new Array(MAIA_MODELS.length).fill(0)
+          }
+          maiaEvals[move][index] = probability
+        })
       }
-    } else if (absCP < 150) {
-      if (cpAdvantage === playerColor) {
-        evaluation = `${playerColor} has a clear positional advantage that could be decisive with careful play.`
-      } else {
-        evaluation = `${playerColor} must play accurately as ${opponent} holds a clear positional advantage.`
-      }
-    } else if (absCP < 300) {
-      if (cpAdvantage === playerColor) {
-        evaluation = `${playerColor} has a significant advantage (${Math.round(toMoveWinrate * 100)}% win rate) that should be convertible with proper technique.`
-      } else {
-        evaluation = `${playerColor} faces a difficult position as ${opponent} has a significant advantage (${Math.round((1 - toMoveWinrate) * 100)}% win rate).`
-      }
-    } else if (absCP < 500) {
-      if (cpAdvantage === playerColor) {
-        evaluation = `${playerColor} is winning and only needs to avoid major blunders to convert.`
-      } else {
-        evaluation = `${playerColor} is in serious trouble and needs to find resilient defensive moves.`
-      }
-    } else {
-      if (cpAdvantage === playerColor) {
-        evaluation = `${playerColor} has a completely winning position with a ${Math.round(toMoveWinrate * 100)}% win probability.`
-      } else {
-        evaluation = `${playerColor} faces a nearly lost position with only a ${Math.round(toMoveWinrate * 100)}% win probability.`
-      }
-    }
-
-    // Suggestion/description of move quality
-    if (isVeryBlunderProne) {
-      suggestion = `This critical position is extremely treacherous with a ${blunderProbability.toFixed(0)}% chance of ${playerColor} making a significant error.`
-    } else if (isBlunderProne && isTacticalPosition) {
-      suggestion = `The sharp tactical nature of this position creates many opportunities for mistakes (${blunderProbability.toFixed(0)}% blunder chance).`
-    } else if (isBlunderProne) {
-      suggestion = `This position is quite treacherous with ${blunderProbability.toFixed(0)}% chance of ${playerColor} making a significant mistake.`
-    } else if (isForcedPlay) {
-      const moveSan = colorSanMapping[topMaiaMove[0]]?.san || topMaiaMove[0]
-      suggestion = `${playerColor} must play ${moveSan}, as all other moves lead to a significantly worse position.`
-    } else if (isTacticalPosition && maiaMatchesStockfish) {
-      const moveSan = colorSanMapping[topMaiaMove[0]]?.san || topMaiaMove[0]
-      suggestion = `The tactical complexity demands precision, with ${moveSan} being the only move that maintains the balance.`
-    } else if (isPositionComplicated && hasMultipleGoodMoves) {
-      suggestion = `This complex position offers several equally promising continuations for ${playerColor}.`
-    } else if (isPositionComplicated) {
-      suggestion = `This is a complex position requiring careful calculation of the many reasonable options.`
-    } else if (isClearBestMove && maiaMatchesStockfish && maiaModelsAgree) {
-      const moveSan = colorSanMapping[topMaiaMove[0]]?.san || topMaiaMove[0]
-      suggestion = `Players of all levels agree ${moveSan} stands out as clearly best in this position.`
-    } else if (isClearBestMove && maiaMatchesStockfish) {
-      const moveSan = colorSanMapping[topMaiaMove[0]]?.san || topMaiaMove[0]
-      suggestion = `${playerColor} should play ${moveSan}, which both human intuition and concrete calculation confirm as best.`
-    } else if (isClearBestMove && maiaModelsAgree) {
-      const moveSan = colorSanMapping[topMaiaMove[0]]?.san || topMaiaMove[0]
-      suggestion = `Human players at all levels strongly prefer ${moveSan} (${topMaiaProbability.toFixed(0)}%), though the engine suggests otherwise.`
-    } else if (isClearBestMove) {
-      const moveSan = colorSanMapping[topMaiaMove[0]]?.san || topMaiaMove[0]
-      suggestion = `Maia strongly suggests ${moveSan} (${topMaiaProbability.toFixed(0)}% likely), though Stockfish calculates a different approach.`
-    } else if (goodProbability > 80) {
-      suggestion = `This is a forgiving position where almost any reasonable move by ${playerColor} maintains the evaluation.`
-    } else if (goodProbability > 60) {
-      suggestion = `Most moves ${playerColor} is likely to consider will maintain the current position assessment.`
-    } else if (maiaMatchesStockfish) {
-      const moveSan = colorSanMapping[topMaiaMove[0]]?.san || topMaiaMove[0]
-      suggestion = `Both human intuition and engine calculation agree that ${moveSan} is the best continuation here.`
-    } else if (hasMultipleGoodMoves) {
-      suggestion = `${playerColor} has several equally strong options, suggesting flexibility in planning.`
-    } else if (top3MaiaProbability < 50) {
-      suggestion = `This unusual position creates difficulties for human calculation, with no clearly favored continuation.`
-    } else {
-      suggestion = `There are several reasonable options for ${playerColor} to consider in this position.`
-    }
+    })
 
-    return `${evaluation} ${suggestion}`
-  }, [currentNode, moveEvaluation, blunderMeter, colorSanMapping])
+    return describePosition(fen, stockfishEvals, maiaEvals, whiteToMove)
+  }, [currentNode, moveEvaluation])
 }
diff --git a/src/hooks/useAnalysisController/useDescriptionGenerator.ts b/src/hooks/useAnalysisController/useDescriptionGenerator.ts
new file mode 100644
index 00000000..efb27ff1
--- /dev/null
+++ b/src/hooks/useAnalysisController/useDescriptionGenerator.ts
@@ -0,0 +1,157 @@
+import { Chess, PieceSymbol } from 'chess.ts'
+
+type StockfishEvals = Record<string, number>
+type MaiaEvals = Record<string, number[]>
+
+const A = 1
+const B = 0.8
+const EPS = 0.08
+
+const winRate = (p: number) => 1 / (1 + Math.exp(-(p - A) / B))
+const wdl = (p: number) => {
+  const w = winRate(p)
+  const l = winRate(-p)
+  return { w, d: 1 - w - l }
+}
+
+export function describePosition(
+  fen: string,
+  sf: StockfishEvals,
+  maia: MaiaEvals,
+  whiteToMove: boolean,
+  eps = EPS,
+): string {
+  const chess = new Chess(fen)
+
+  const legal = new Set<string>()
+  chess
+    .moves({ verbose: true })
+    .forEach((m) => legal.add(m.from + m.to + (m.promotion ?? '')))
+
+  const moves = Object.keys(sf).filter((m) => legal.has(m))
+  if (!moves.length) return 'No legal moves available.'
+
+  const seval: Record<string, number> = {}
+  moves.forEach((m) => {
+    seval[m] = (whiteToMove ? 1 : 1) * sf[m]
+  })
+
+  const opt = moves.reduce((a, b) => (seval[a] > seval[b] ? a : b))
+  const { w: wOpt, d: dOpt } = wdl(seval[opt])
+
+  const good = moves.filter((m) => {
+    const { w, d } = wdl(seval[m])
+    return Math.abs(w - wOpt) <= eps && Math.abs(d - dOpt) <= eps
+  })
+
+  const nGood = good.length
+  const abundance =
+    nGood === 1 ? 'only one move' : nGood === 2 ? 'two moves' : 'several moves'
+
+  const uciToSan = (uci: string): string => {
+    const from = uci.slice(0, 2)
+    const to = uci.slice(2, 4)
+    const promotion = uci.length > 4 ? uci[4] : undefined
+    const mv = chess.move({ from, to, promotion: promotion as PieceSymbol })
+    const san = mv?.san ?? uci
+    chess.undo()
+    return san
+  }
+
+  const bestGoodMoves = [...good]
+    .sort((a, b) => seval[b] - seval[a])
+    .slice(0, 3)
+  const moveList = bestGoodMoves.map(uciToSan).join(', ')
+  const bestMoveSan = uciToSan(opt)
+
+  const avgGood = good.reduce((s, m) => s + seval[m], 0) / nGood
+
+  let outcome: string
+  if (avgGood > 2.5) outcome = 'to cleanly win'
+  else if (avgGood > 1.0) outcome = 'to win'
+  else if (avgGood > 0.35) outcome = 'for an advantage'
+  else if (avgGood >= -0.35) outcome = 'to keep the balance'
+  else if (avgGood >= -1.0) outcome = 'to hold the position'
+  else outcome = 'to stay in the game'
+
+  let setLevels = 0
+  let optLevels = 0
+  let temptLevels = 0
+  const temptCount: Record<string, number> = {}
+
+  for (let lvl = 0; lvl < 9; lvl++) {
+    const probs = moves
+      .map((m) => [maia[m]?.[lvl] ?? 0, m] as [number, string])
+      .sort((a, b) => b[0] - a[0])
+
+    const [p1, m1] = probs[0]
+    const [p2, m2] = probs[1] ?? [0, '']
+    const [p3, m3] = probs[2] ?? [0, '']
+
+    const inGood = good.includes(m1)
+    if (inGood) setLevels++
+    if (m1 === opt) optLevels++
+
+    const nearTop = (prob: number) => p1 - prob <= eps
+    const addTempt = (uci: string) => {
+      temptCount[uci] = (temptCount[uci] ?? 0) + 1
+      return true
+    }
+
+    const tempting =
+      inGood &&
+      ((m2 && !good.includes(m2) && nearTop(p2) && addTempt(m2)) ||
+        (m3 && !good.includes(m3) && nearTop(p3) && addTempt(m3)))
+
+    if (tempting) temptLevels++
+  }
+
+  const tier = (k: number) => (k <= 2 ? 0 : k <= 6 ? 1 : 2)
+  const setTier = tier(setLevels)
+  const optTier = tier(optLevels)
+
+  const phrSet =
+    setTier === 0
+      ? 'hard for human players to find'
+      : setTier === 1
+        ? 'findable for skilled players'
+        : 'straightforward for players across skill levels to find'
+
+  let phrBest =
+    optTier === 0
+      ? 'hard for human players to find'
+      : optTier === 1
+        ? 'findable for skilled players'
+        : 'straightforward for players across skill levels to find'
+
+  if (optTier === 1 && optTier < setTier) phrBest = 'only ' + phrBest
+
+  const verb = nGood === 1 ? 'is' : 'are'
+  const pron = nGood === 1 ? 'it is' : 'they are'
+
+  let temptText = ''
+  const hasTempting = setLevels > 0 && temptLevels > setLevels / 2
+  if (hasTempting) {
+    const topTemptUci = Object.entries(temptCount).sort(
+      (a, b) => b[1] - a[1],
+    )[0]?.[0]
+    const temptSan = topTemptUci ? uciToSan(topTemptUci) : ''
+    temptText =
+      temptSan !== ''
+        ? ` There are also tempting alternatives, such as ${temptSan}.`
+        : ' There are also tempting alternatives.'
+    if (!(optTier < setTier) && setTier == 2) {
+      temptText = ` However, there are tempting alternatives, such as ${temptSan}.`
+    }
+  }
+
+  if (nGood === 1) {
+    return `There ${verb} ${abundance} (${moveList}) ${outcome}, and ${pron} ${phrSet}.${temptText}`
+  }
+
+  if (optTier < setTier) {
+    return `There ${verb} ${abundance} (${moveList}) ${outcome}, and ${pron} ${phrSet}, but the best move (${bestMoveSan}) is ${phrBest}.${temptText}`
+  }
+
+  return `There ${verb} ${abundance} (${moveList}) ${outcome}, and ${pron} ${phrSet}.${temptText}`
+}