# All files / src/tools sentimentTracker.ts

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     /**
     * MCP Tool: sentiment_tracker
     *
     * Track political group sentiment over a configurable time window using a
     * weighted blend of (a) seat-share momentum derived from the current EP API
     * MEP composition and (b) observed voting cohesion / defection rates derived
     * from EP DOCEO roll-call vote (RCV) XML. The previously informational-only
     * `timeframe` parameter now drives a real DOCEO window: votes outside the
     * `last_month`/`last_quarter`/`last_year` cutoff are excluded, and the tool
     * splits the window into halves and sub-windows to compute trend and
     * volatility deterministically.
     *
     * **Intelligence Perspective:** Real RCV-derived cohesion provides direct
     * visibility into intra-group dissent, defection patterns and emerging
     * fractures — substantially more reliable than pure seat-share proxies.
     *
     * **Business Perspective:** Supports policy consultancies, financial
     * analysts, and corporate affairs teams who need timely, methodology-sound
     * reads on group discipline for lobbying strategy and regulatory risk
     * assessment.
     *
     * **Marketing Perspective:** Appeals to journalists, academic researchers,
     * and civic-tech developers as a transparent, time-windowed barometer with
     * explicit fallback semantics when DOCEO coverage is sparse.
     *
     * ISMS Policy: SC-002 (Input Validation), AC-003 (Least Privilege),
     *   AU-002 (Audit Logging). GDPR Article 5(1)(d) — accuracy principle.
     */

    import { z } from 'zod';
    import { buildToolResponse, buildErrorResponse } from './shared/responseBuilder.js';
    import type { ToolResult } from './shared/types.js';
    import { fetchAllCurrentMEPs } from '../utils/mepFetcher.js';
    import { doceoClient } from '../clients/ep/doceoClient.js';
    import { normalizePoliticalGroup } from '../utils/politicalGroupNormalization.js';
    import { withTimeoutAndAbort } from '../utils/timeout.js';
    import type { LatestVoteRecord } from '../clients/ep/doceoXmlParser.js';

    /**
     * Zod input schema for the `sentiment_tracker` MCP tool. Optional
     * `groupId` filters the analysis to a single political group; `timeframe`
     * selects the DOCEO RCV aggregation window (last 30 / 90 / 365 days).
     */
    export const SentimentTrackerSchema = z.object({
      groupId: z.string()
        .min(1)
        .max(50)
        .optional()
        .describe('Political group ID to track (omit for all groups)'),
      timeframe: z.enum(['last_month', 'last_quarter', 'last_year'])
        .optional()
        .default('last_quarter')
        .describe('DOCEO roll-call vote aggregation window: last_month (~30d), last_quarter (~90d), or last_year (~365d). Falls back to seat-share-only proxy with confidenceLevel=LOW when DOCEO coverage is insufficient.'),
    });

    /**
     * Validated parameter type for the `sentiment_tracker` tool, inferred
     * from {@link SentimentTrackerSchema}.
     */
    export type SentimentTrackerParams = z.infer<typeof SentimentTrackerSchema>;

    type Timeframe = NonNullable<SentimentTrackerParams['timeframe']>;
    type Trend = 'IMPROVING' | 'STABLE' | 'DECLINING' | 'VOLATILE';

    interface GroupSentiment {
      groupId: string;
      sentimentScore: number;
      trend: Trend;
      volatility: number;
      memberCount: number;
      cohesionProxy: number;
    }

    /** Internal type extending GroupSentiment with unrounded cohesion for aggregation. */
    interface InternalGroupSentiment extends GroupSentiment {
      _rawCohesion: number;
    }

    interface SentimentShift {
      groupId: string;
      fromScore: number;
      toScore: number;
      magnitude: number;
      direction: 'POSITIVE' | 'NEGATIVE';
      estimatedCause: string;
    }

    interface SentimentTrackerResult {
      timeframe: string;
      groupSentiments: GroupSentiment[];
      polarizationIndex: number;
      consensusTopics: string[];
      divisiveTopics: string[];
      sentimentShifts: SentimentShift[];
      overallParliamentSentiment: number;
      computedAttributes: {
        mostPositiveGroup: string;
        mostNegativeGroup: string;
        highestVolatility: string;
        trendingSentiment: 'POSITIVE' | 'NEGATIVE' | 'NEUTRAL';
        bimodalityIndex: number;
      };
      dataAvailable: boolean;
      confidenceLevel: 'HIGH' | 'MEDIUM' | 'LOW';
      dataFreshness: string;
      sourceAttribution: string;
      methodology: string;
      dataQualityWarnings: string[];
    }

    const KNOWN_POLITICAL_GROUPS = ['EPP', 'S&D', 'Renew', 'Greens/EFA', 'ECR', 'PfE', 'The Left', 'ESN', 'NI'];

    /** Per-timeframe DOCEO fetch and aggregation parameters. */
    const TIMEFRAME_CONFIG: Record<Timeframe, {
      days: number;
      maxWeeks: number;
      voteBudget: number;
      subWindows: number;
    }> = {
      last_month: { days: 30, maxWeeks: 2, voteBudget: 100, subWindows: 2 },
      last_quarter: { days: 90, maxWeeks: 4, voteBudget: 200, subWindows: 3 },
      last_year: { days: 365, maxWeeks: 8, voteBudget: 300, subWindows: 4 },
    } as const;

    /** Minimum DOCEO RCV votes within the window required to upgrade out of LOW confidence. */
    const MIN_RCV_VOTES_FOR_MEDIUM = 10;
    const MIN_RCV_VOTES_FOR_HIGH = 40;

    /** DOCEO call timeout (mirrors `analyzeCoalitionDynamics` budget). */
    const DOCEO_TIMEOUT_MS = 4_000;
    /** In-memory cache TTL for the (timeframe-keyed) DOCEO window aggregate. */
    const DOCEO_CACHE_TTL_MS = 5 * 60_000;

    /** Cohesion bands used to surface DOCEO vote subjects as consensus/divisive topics. */
    const CONSENSUS_TOPIC_COHESION_MIN = 0.95;
    const DIVISIVE_TOPIC_COHESION_MAX = 0.55;

    /** Sentiment score component weights (must sum to 1.0). */
    const SCORE_WEIGHTS = {
      cohesion: 0.5,
      dissent: 0.2,
      seatShareMomentum: 0.3,
    } as const;

    /** Seat-share thresholds used only on the fallback path (no DOCEO data). */
    const SEAT_SHARE_THRESHOLDS = { large: 0.25, medium: 0.15, small: 0.05 } as const;
    const SEAT_SHARE_FALLBACK_SCORES = { large: 0.3, medium: 0.2, small: 0.1, micro: -0.1 } as const;

    /** Trend classification thresholds. */
    const TREND_DELTA_THRESHOLD = 0.05;
    const VOLATILITY_VARIANCE_THRESHOLD = 0.1;

    interface DoceoWindowAggregate {
      /** Per-group cohesion stats for the full window. */
      groupStats: Map<string, { cohesion: number; rcvVotes: number; topics: string[] }>;
      /** Per-group cohesion for the first half of the window (oldest). */
      firstHalfCohesion: Map<string, number>;
      /** Per-group cohesion for the second half of the window (newest). */
      secondHalfCohesion: Map<string, number>;
      /** Per-group cohesion variance across N sub-windows (drives VOLATILE classification). */
      cohesionVariance: Map<string, number>;
      /** Per-group consensus vote subjects (cohesion ≥ CONSENSUS_TOPIC_COHESION_MIN). */
      consensusTopics: string[];
      /** Per-group divisive vote subjects (cohesion ≤ DIVISIVE_TOPIC_COHESION_MAX). */
      divisiveTopics: string[];
      /** Total RCV votes inspected within the window. */
      rcvVoteCount: number;
      /** True when DOCEO returned no RCV-backed records inside the window. */
      empty: boolean;
    }

    let doceoWindowCache:
      | { expiresAt: number; key: string; value: DoceoWindowAggregate }
      | undefined;

    /**
     * Test-only hook for resetting the bounded DOCEO window cache between specs.
     * @internal
     */
    export function clearSentimentTrackerDoceoCache(): void {
      doceoWindowCache = undefined;
    }

    /**
     * Format a `Date` as `YYYY-MM-DD` in UTC.
     */
    function formatDateIsoUtc(d: Date): string {
      return d.toISOString().slice(0, 10);
    }

    /**
     * Compute the Monday of the plenary week containing `now`.
     */
    function mostRecentMondayUtc(now: Date): string {
      const day = now.getUTCDay();
      const daysBack = day === 0 ? 6 : day - 1;
      const monday = new Date(now);
      monday.setUTCDate(now.getUTCDate() - daysBack);
      return formatDateIsoUtc(monday);
    }

    /**
     * Subtract `days` UTC days from a `YYYY-MM-DD` date.
     */
    function subtractDaysIso(isoDate: string, days: number): string {
      const d = new Date(`${isoDate}T00:00:00Z`);
      d.setUTCDate(d.getUTCDate() - days);
      return formatDateIsoUtc(d);
    }

    /**
     * Compute majority cohesion for a single group breakdown row.
     * Cohesion = `max(for, against, abstain) / total` ∈ [0, 1].
     * Returns `null` when no votes were recorded for the group on this vote.
     */
    function rowCohesion(
      row: { for: number; against: number; abstain: number } | undefined
    ): number | null {
      if (row === undefined) return null;
      const total = row.for + row.against + row.abstain;
      Iif (total === 0) return null;
      return Math.max(row.for, row.against, row.abstain) / total;
    }

    /**
     * Locate the cohesion contribution for `normalizedGroup` in a raw DOCEO group
     * breakdown record. Falls back to alias-aware matching via {@link normalizePoliticalGroup}.
     */
    function resolveGroupRow(
      breakdown: Record<string, { for: number; against: number; abstain: number }> | undefined,
      normalizedGroup: string
    ): { for: number; against: number; abstain: number } | undefined {
      Iif (breakdown === undefined) return undefined;
      if (normalizedGroup in breakdown) return breakdown[normalizedGroup];
      for (const [rawKey, row] of Object.entries(breakdown)) {
        Iif (normalizePoliticalGroup(rawKey) === normalizedGroup) return row;
      }
      return undefined;
    }

    /**
     * Process a single DOCEO response page, filtering RCV records by date and
     * appending them to `collected` until the vote budget is reached.
     * Returns `{ stop: true }` when the budget is exhausted.
     * Records with missing/empty dates are treated as out-of-window and skipped.
     * Pre-cutoff records are filtered out but do NOT terminate iteration (DOCEO
     * responses are not guaranteed to be sorted newest→oldest within a week).
     */
    function ingestDoceoPage(
      response: { data: readonly LatestVoteRecord[] },
      cutoff: string,
      voteBudget: number,
      collected: LatestVoteRecord[]
    ): { stop: boolean } {
      for (const vote of response.data) {
        Iif (vote.dataSource !== 'RCV') continue;
        Iif (vote.groupBreakdown === undefined) continue;
        const date = vote.sittingDate ?? vote.date;
        // Treat missing/empty dates as out-of-window (skip without stopping).
        Iif (date === '') continue;
        // Filter out pre-cutoff votes without stopping iteration.
        if (date < cutoff) continue;
        collected.push(vote);
        Iif (collected.length >= voteBudget) return { stop: true };
      }
      return { stop: false };
    }

    /**
     * Iterate plenary weeks backwards from the current week, fetching DOCEO RCV
     * records and filtering them to the time window `[cutoff, now]`. Stops on
     * vote-budget or `maxWeeks` boundary, whichever comes first.
     */
    async function fetchWindowedDoceoVotes(
      timeframe: Timeframe,
      abortSignal: AbortSignal,
      now: Date = new Date()
    ): Promise<LatestVoteRecord[]> {
      const cfg = TIMEFRAME_CONFIG[timeframe];
      const cutoff = subtractDaysIso(formatDateIsoUtc(now), cfg.days);
      const collected: LatestVoteRecord[] = [];
      let weekStart = mostRecentMondayUtc(now);

      for (let weekIndex = 0; weekIndex < cfg.maxWeeks && collected.length < cfg.voteBudget; weekIndex++) {
        const response = await doceoClient.getLatestVotes({
          weekStart,
          includeIndividualVotes: false,
          limit: 100,
          abortSignal,
        });
        const { stop } = ingestDoceoPage(response, cutoff, cfg.voteBudget, collected);
        Iif (stop) break;
        weekStart = subtractDaysIso(weekStart, 7);
      }

      return collected.slice(0, cfg.voteBudget);
    }

    /** Sort vote records ascending by sitting date (oldest first), stable on input order ties. */
    function sortVotesByDateAscending(votes: LatestVoteRecord[]): LatestVoteRecord[] {
      const indexed = votes.map((v, i) => ({ v, i, d: v.sittingDate ?? v.date }));
      indexed.sort((a, b) => {
        if (a.d === b.d) return a.i - b.i;
        return a.d < b.d ? -1 : 1;
      });
      return indexed.map(x => x.v);
    }

    /** Compute mean cohesion for a given group over a slice of votes. */
    function meanCohesion(
      votes: readonly LatestVoteRecord[],
      group: string
    ): { mean: number; count: number } {
      let sum = 0;
      let count = 0;
      for (const vote of votes) {
        const c = rowCohesion(resolveGroupRow(vote.groupBreakdown, group));
        if (c !== null) { sum += c; count++; }
      }
      return { mean: count > 0 ? sum / count : 0, count };
    }

    /** Compute sample variance across non-empty sub-window cohesion means for a group. */
    function subWindowVariance(
      votes: readonly LatestVoteRecord[],
      group: string,
      subWindows: number
    ): number {
      if (votes.length < subWindows) return 0;
      const bucketSize = Math.ceil(votes.length / subWindows);
      const means: number[] = [];
      for (let i = 0; i < subWindows; i++) {
        const slice = votes.slice(i * bucketSize, (i + 1) * bucketSize);
        const { mean, count } = meanCohesion(slice, group);
        if (count > 0) means.push(mean);
      }
      if (means.length < 2) return 0;
      const avg = means.reduce((s, v) => s + v, 0) / means.length;
      const variance = means.reduce((s, v) => s + (v - avg) ** 2, 0) / means.length;
      return variance;
    }

    /** Identify per-vote consensus / divisive subjects for any analyzed group. */
    function extractTopics(
      votes: readonly LatestVoteRecord[],
      analyzedGroups: readonly string[]
    ): { consensus: string[]; divisive: string[] } {
      const consensus = new Set<string>();
      const divisive = new Set<string>();
      for (const vote of votes) {
        const subject = vote.subject.trim();
        Iif (subject === '') continue;
        for (const group of analyzedGroups) {
          const c = rowCohesion(resolveGroupRow(vote.groupBreakdown, group));
          if (c === null) continue;
          if (c >= CONSENSUS_TOPIC_COHESION_MIN) consensus.add(subject);
          else if (c <= DIVISIVE_TOPIC_COHESION_MAX) divisive.add(subject);
        }
      }
      return {
        consensus: [...consensus].slice(0, 10),
        divisive: [...divisive].slice(0, 10),
      };
    }

    /**
     * Build per-group cohesion statistics, half-window splits and sub-window
     * variance from the windowed DOCEO RCV slice.
     */
    function buildDoceoAggregate(
      votes: LatestVoteRecord[],
      analyzedGroups: readonly string[],
      subWindows: number
    ): DoceoWindowAggregate {
      const sorted = sortVotesByDateAscending(votes);
      const mid = Math.floor(sorted.length / 2);
      const firstHalf = sorted.slice(0, mid);
      const secondHalf = sorted.slice(mid);

      const groupStats = new Map<string, { cohesion: number; rcvVotes: number; topics: string[] }>();
      const firstHalfCohesion = new Map<string, number>();
      const secondHalfCohesion = new Map<string, number>();
      const cohesionVariance = new Map<string, number>();

      for (const group of analyzedGroups) {
        const fullStats = meanCohesion(sorted, group);
        if (fullStats.count > 0) {
          groupStats.set(group, { cohesion: fullStats.mean, rcvVotes: fullStats.count, topics: [] });
        }
        const firstStats = meanCohesion(firstHalf, group);
        if (firstStats.count > 0) firstHalfCohesion.set(group, firstStats.mean);
        const secondStats = meanCohesion(secondHalf, group);
        if (secondStats.count > 0) secondHalfCohesion.set(group, secondStats.mean);
        cohesionVariance.set(group, subWindowVariance(sorted, group, subWindows));
      }

      const { consensus, divisive } = extractTopics(sorted, analyzedGroups);

      return {
        groupStats,
        firstHalfCohesion,
        secondHalfCohesion,
        cohesionVariance,
        consensusTopics: consensus,
        divisiveTopics: divisive,
        rcvVoteCount: sorted.length,
        empty: sorted.length === 0,
      };
    }

    function emptyDoceoAggregate(): DoceoWindowAggregate {
      return {
        groupStats: new Map(),
        firstHalfCohesion: new Map(),
        secondHalfCohesion: new Map(),
        cohesionVariance: new Map(),
        consensusTopics: [],
        divisiveTopics: [],
        rcvVoteCount: 0,
        empty: true,
      };
    }

    /**
     * Fetch the DOCEO window aggregate for the requested timeframe with a TTL
     * cache. Failures are swallowed — the caller falls back to the seat-share
     * proxy path with `confidenceLevel: 'LOW'`.
     */
    async function loadDoceoWindowAggregate(
      timeframe: Timeframe,
      analyzedGroups: readonly string[]
    ): Promise<DoceoWindowAggregate> {
      const now = Date.now();
      const key = `${timeframe}::${analyzedGroups.join(',')}`;
      Iif (doceoWindowCache?.key === key && doceoWindowCache.expiresAt > now) {
        return doceoWindowCache.value;
      }

      try {
        const votes = await withTimeoutAndAbort(
          (signal) => fetchWindowedDoceoVotes(timeframe, signal),
          DOCEO_TIMEOUT_MS,
          'DOCEO window aggregation timed out'
        );
        const aggregate = buildDoceoAggregate(votes, analyzedGroups, TIMEFRAME_CONFIG[timeframe].subWindows);
        doceoWindowCache = { expiresAt: now + DOCEO_CACHE_TTL_MS, key, value: aggregate };
        return aggregate;
      } catch {
        const aggregate = emptyDoceoAggregate();
        doceoWindowCache = { expiresAt: now + DOCEO_CACHE_TTL_MS, key, value: aggregate };
        return aggregate;
      }
    }

    /** Seat-share fallback score used when DOCEO data is insufficient. */
    function fallbackSeatShareScore(memberCount: number, totalMEPs: number): number {
      const seatShare = totalMEPs > 0 ? memberCount / totalMEPs : 0;
      if (seatShare > SEAT_SHARE_THRESHOLDS.large) return SEAT_SHARE_FALLBACK_SCORES.large;
      if (seatShare > SEAT_SHARE_THRESHOLDS.medium) return SEAT_SHARE_FALLBACK_SCORES.medium;
      if (seatShare > SEAT_SHARE_THRESHOLDS.small) return SEAT_SHARE_FALLBACK_SCORES.small;
      return SEAT_SHARE_FALLBACK_SCORES.micro;
    }

    /** Seat-share-only trend fallback (no DOCEO half-window data). */
    function fallbackSeatShareTrend(seatShare: number): Trend {
      if (seatShare > 0.25) return 'STABLE';
      if (seatShare > 0.15) return 'IMPROVING';
      if (seatShare > 0.05) return 'STABLE';
      return 'DECLINING';
    }

    /**
     * Classify a per-group trend using DOCEO half-window cohesion delta and
     * sub-window variance. Returns `null` when the DOCEO data is insufficient.
     */
    function classifyDoceoTrend(
      firstHalf: number | undefined,
      secondHalf: number | undefined,
      variance: number
    ): Trend | null {
      if (variance > VOLATILITY_VARIANCE_THRESHOLD) return 'VOLATILE';
      Iif (firstHalf === undefined || secondHalf === undefined) return null;
      const delta = secondHalf - firstHalf;
      if (delta > TREND_DELTA_THRESHOLD) return 'IMPROVING';
      if (delta < -TREND_DELTA_THRESHOLD) return 'DECLINING';
      return 'STABLE';
    }

    /** Clamp a real-valued score into `[-1, +1]`. */
    function clampScore(value: number): number {
      return Math.max(-1, Math.min(1, value));
    }

    /** Round a number to 2 decimal places. */
    function round2(value: number): number {
      return Math.round(value * 100) / 100;
    }

    /**
     * Compute the per-group sentiment score as a weighted blend of:
     * - DOCEO cohesion (centred at 0.5 → score range `[-0.5, +0.5]`),
     * - dissent / defection rate (inverse cohesion penalty, range `[-0.5, +0.5]`),
     * - seat-share momentum (linear in seat share, range `[-0.5, +0.5]`).
     */
    function computeBlendedScore(
      cohesion: number,
      seatShare: number
    ): number {
      // Cohesion: 0.5 cohesion → 0 contribution, 1.0 → +0.5, 0.0 → -0.5.
      const cohesionTerm = (cohesion - 0.5) * 2 * 0.5;
      // Dissent: penalty proportional to (1 - cohesion), inverted.
      const dissentTerm = (0.5 - (1 - cohesion)) * 0.5;
      // Seat-share momentum: 0% → -0.5, 50% → +0.5.
      const seatShareTerm = (seatShare - 0.25) * 2;
      const raw =
        SCORE_WEIGHTS.cohesion * cohesionTerm +
        SCORE_WEIGHTS.dissent * dissentTerm +
        SCORE_WEIGHTS.seatShareMomentum * seatShareTerm;
      return clampScore(raw);
    }

    interface BuildGroupSentimentArgs {
      groupId: string;
      memberCount: number;
      totalMEPs: number;
      doceo: DoceoWindowAggregate;
      hasDoceoCoverage: boolean;
    }

    /** Build the per-group sentiment record using DOCEO data when available. */
    function buildGroupSentiment(args: BuildGroupSentimentArgs): InternalGroupSentiment {
      const { groupId, memberCount, totalMEPs, doceo, hasDoceoCoverage } = args;
      const seatShare = totalMEPs > 0 ? memberCount / totalMEPs : 0;
      const stats = doceo.groupStats.get(groupId);

      if (!hasDoceoCoverage || stats === undefined) {
        const fallbackScore = fallbackSeatShareScore(memberCount, totalMEPs);
        const rawCohesion = 0.5 + fallbackScore * 0.3;
        return {
          groupId,
          sentimentScore: round2(fallbackScore),
          trend: fallbackSeatShareTrend(seatShare),
          volatility: 0.12,
          memberCount,
          cohesionProxy: round2(rawCohesion),
          _rawCohesion: rawCohesion,
        };
      }

      const cohesion = stats.cohesion;
      const variance = doceo.cohesionVariance.get(groupId) ?? 0;
      const trend = classifyDoceoTrend(
        doceo.firstHalfCohesion.get(groupId),
        doceo.secondHalfCohesion.get(groupId),
        variance
      ) ?? fallbackSeatShareTrend(seatShare);
      const score = computeBlendedScore(cohesion, seatShare);
      return {
        groupId,
        sentimentScore: round2(score),
        trend,
        volatility: round2(Math.sqrt(variance)),
        memberCount,
        cohesionProxy: round2(cohesion),
        _rawCohesion: cohesion,
      };
    }

    /**
     * Compute polarization as `1 − seat-share-weighted mean of cohesion across
     * analyzed groups that have observed DOCEO cohesion`. Falls back to
     * seat-share-score dispersion when DOCEO cohesion is unavailable. Only groups
     * with actual DOCEO observations are weighted into the polarization index;
     * groups whose `cohesionProxy` is a fallback value are excluded so the metric
     * remains a faithful measure of observed group cohesion.
     */
    function computePolarizationIndex(
      sentiments: InternalGroupSentiment[],
      totalMEPs: number,
      hasDoceoCoverage: boolean,
      doceo: DoceoWindowAggregate
    ): number {
      Iif (sentiments.length === 0) return 0;
      if (hasDoceoCoverage && totalMEPs > 0) {
        let weightedCohesion = 0;
        let totalWeight = 0;
        for (const g of sentiments) {
          if (!doceo.groupStats.has(g.groupId)) continue;
          const weight = g.memberCount / totalMEPs;
          weightedCohesion += g._rawCohesion * weight;
          totalWeight += weight;
        }
        Iif (totalWeight === 0) return 0;
        const mean = weightedCohesion / totalWeight;
        return round2(Math.max(0, Math.min(1, 1 - mean)));
      }
      // Fallback: dispersion of fallback seat-share scores.
      if (sentiments.length < 2) return 0;
      const scores = sentiments.map(s => s.sentimentScore);
      const mean = scores.reduce((s, v) => s + v, 0) / scores.length;
      const variance = scores.reduce((s, v) => s + (v - mean) ** 2, 0) / scores.length;
      return Math.min(1, Math.round(Math.sqrt(variance) * 200) / 100);
    }

    function computeBimodalityIndex(sentiments: number[]): number {
      if (sentiments.length < 3) return 0;
      const extreme = sentiments.filter(s => s > 0.1 || s < -0.1).length;
      return Math.round((extreme / sentiments.length) * 100) / 100;
    }

    function buildSentimentShifts(
      sentiments: GroupSentiment[],
      hasDoceoCoverage: boolean
    ): SentimentShift[] {
      const shifts: SentimentShift[] = [];
      for (const g of sentiments) {
        const magnitude = Math.abs(g.sentimentScore);
        if (magnitude > 0.15) {
          shifts.push({
            groupId: g.groupId,
            fromScore: 0,
            toScore: g.sentimentScore,
            magnitude: Math.round(magnitude * 100) / 100,
            direction: g.sentimentScore > 0 ? 'POSITIVE' : 'NEGATIVE',
            estimatedCause: estimateShiftCause(g, hasDoceoCoverage),
          });
        }
      }
      return shifts.sort((a, b) => b.magnitude - a.magnitude).slice(0, 5);
    }

    function estimateShiftCause(g: GroupSentiment, hasDoceoCoverage: boolean): string {
      if (!hasDoceoCoverage) {
        return g.sentimentScore > 0
          ? 'Large seat share indicates strong institutional position on tracked topics'
          : 'Smaller seat share indicates weaker institutional position or limited leverage on tracked topics';
      }
      Iif (g.trend === 'VOLATILE') {
        return 'High variance in DOCEO group cohesion across sub-windows';
      }
      Eif (g.sentimentScore > 0) {
        return `High DOCEO cohesion (${String(g.cohesionProxy)}) combined with seat-share position`;
      }
      return `Low DOCEO cohesion (${String(g.cohesionProxy)}) signals dissent / defection within the window`;
    }

    function classifyTrendingSentiment(score: number): 'POSITIVE' | 'NEGATIVE' | 'NEUTRAL' {
      if (score > 0.1) return 'POSITIVE';
      Iif (score < -0.1) return 'NEGATIVE';
      return 'NEUTRAL';
    }

    function determineConfidenceLevel(rcvVoteCount: number): 'HIGH' | 'MEDIUM' | 'LOW' {
      if (rcvVoteCount >= MIN_RCV_VOTES_FOR_HIGH) return 'HIGH';
      if (rcvVoteCount >= MIN_RCV_VOTES_FOR_MEDIUM) return 'MEDIUM';
      return 'LOW';
    }

    function buildEmptySentimentResult(params: SentimentTrackerParams): SentimentTrackerResult {
      return {
        timeframe: params.timeframe,
        groupSentiments: [],
        polarizationIndex: 0,
        consensusTopics: [],
        divisiveTopics: [],
        sentimentShifts: [],
        overallParliamentSentiment: 0,
        computedAttributes: {
          mostPositiveGroup: 'N/A',
          mostNegativeGroup: 'N/A',
          highestVolatility: 'N/A',
          trendingSentiment: 'NEUTRAL',
          bimodalityIndex: 0
        },
        dataAvailable: false,
        confidenceLevel: 'LOW',
        dataFreshness: 'No data available from EP API',
        sourceAttribution: 'European Parliament Open Data Portal - data.europarl.europa.eu',
        methodology: 'Sentiment derived from seat-share proxies — no MEP data available.',
        dataQualityWarnings: ['No MEP data available from EP API — all sentiment scores are empty'],
      };
    }

    function buildSentimentComputedAttrs(
      validSentiments: GroupSentiment[],
      sentimentScores: number[],
      overallSentiment: number
    ): SentimentTrackerResult['computedAttributes'] {
      const sortedBySentiment = [...validSentiments].sort((a, b) => b.sentimentScore - a.sentimentScore);
      const sortedByVolatility = [...validSentiments].sort((a, b) => b.volatility - a.volatility);
      return {
        mostPositiveGroup: sortedBySentiment[0]?.groupId ?? 'N/A',
        mostNegativeGroup: sortedBySentiment[sortedBySentiment.length - 1]?.groupId ?? 'N/A',
        highestVolatility: sortedByVolatility[0]?.groupId ?? 'N/A',
        trendingSentiment: classifyTrendingSentiment(overallSentiment),
        bimodalityIndex: computeBimodalityIndex(sentimentScores)
      };
    }

    function buildMethodology(timeframe: Timeframe, hasDoceoCoverage: boolean, rcvVoteCount: number): string {
      const window = `${String(TIMEFRAME_CONFIG[timeframe].days)}d`;
      if (!hasDoceoCoverage) {
        const coverageNote = rcvVoteCount > 0
          ? `insufficient DOCEO RCV coverage in ${window} window — only ${String(rcvVoteCount)} usable RCVs, minimum ${String(MIN_RCV_VOTES_FOR_MEDIUM)} required`
          : `no DOCEO RCV votes found in ${window} window`;
        return [
          `FALLBACK PATH (${coverageNote}).`,
          'Sentiment scores fall back to seat-share proxy (larger group → stronger institutional position).',
          'Trend defaults to seat-share band. polarizationIndex = std-dev of fallback scores.',
          'Data source (composition): https://data.europarl.europa.eu/api/v2/meps',
        ].join(' ');
      }
      return [
        `Sentiment score = ${String(SCORE_WEIGHTS.cohesion)} · DOCEO cohesion (centred at 0.5)`,
        `+ ${String(SCORE_WEIGHTS.dissent)} · inverse-dissent term`,
        `+ ${String(SCORE_WEIGHTS.seatShareMomentum)} · seat-share momentum, clamped to [-1, +1].`,
        `Trend derived from half-window cohesion delta (>${String(TREND_DELTA_THRESHOLD)} → IMPROVING/DECLINING) `,
        `with VOLATILE when sub-window cohesion variance > ${String(VOLATILITY_VARIANCE_THRESHOLD)}.`,
        `polarizationIndex = 1 − seat-share-weighted mean(cohesion) across analyzed groups.`,
        `Window: ${window}, ${String(rcvVoteCount)} DOCEO RCVs inspected.`,
        'Data sources: https://data.europarl.europa.eu/api/v2/meps (composition); EP DOCEO RCV XML (cohesion).',
      ].join(' ');
    }

    interface SentimentBuildContext {
      groupCounts: Map<string, number>;
      totalMEPs: number;
      doceo: DoceoWindowAggregate;
      hasDoceoCoverage: boolean;
    }

    function buildSentimentScores(
      targetGroups: readonly string[],
      ctx: SentimentBuildContext
    ): InternalGroupSentiment[] {
      return targetGroups.map(gId => buildGroupSentiment({
        groupId: gId,
        memberCount: ctx.groupCounts.get(gId) ?? 0,
        totalMEPs: ctx.totalMEPs,
        doceo: ctx.doceo,
        hasDoceoCoverage: ctx.hasDoceoCoverage,
      }));
    }

    function buildDataQualityWarnings(
      hasDoceoCoverage: boolean,
      rcvVoteCount: number,
      mepDataComplete: boolean,
      mepFailureOffset: number | undefined
    ): string[] {
      const warnings: string[] = [];
      if (!hasDoceoCoverage) {
        warnings.push(
          `DOCEO RCV coverage insufficient (${String(rcvVoteCount)} RCVs in window) — sentiment scores fall back to seat-share proxy`
        );
      } else if (rcvVoteCount < MIN_RCV_VOTES_FOR_HIGH) {
        warnings.push(
          `DOCEO RCV coverage limited (${String(rcvVoteCount)} RCVs) — scores reflect a small sample of recent votes`
        );
      }
      Iif (!mepDataComplete) {
        warnings.push(
          `MEP data is incomplete — pagination failed at offset ${String(mepFailureOffset ?? 0)}; results based on partial data`
        );
      }
      return warnings;
    }

    function tallyGroupCounts(allMeps: readonly { politicalGroup: string }[]): Map<string, number> {
      const groupCounts = new Map<string, number>();
      for (const mep of allMeps) {
        const canonical = normalizePoliticalGroup(mep.politicalGroup);
        Iif (canonical === '' || canonical.toLowerCase() === 'unknown') continue;
        groupCounts.set(canonical, (groupCounts.get(canonical) ?? 0) + 1);
      }
      return groupCounts;
    }

    function resolveTargetGroups(groupId: string | undefined): readonly string[] {
      if (groupId === undefined) return KNOWN_POLITICAL_GROUPS;
      const normalized = normalizePoliticalGroup(groupId);
      return [normalized === '' ? groupId : normalized];
    }

    interface SentimentResultArgs {
      params: SentimentTrackerParams;
      fetchResult: { complete: boolean; failureOffset?: number | undefined };
      totalMEPs: number;
      groupSentiments: InternalGroupSentiment[];
      doceo: DoceoWindowAggregate;
      hasDoceoCoverage: boolean;
    }

    function buildSentimentResult(args: SentimentResultArgs): SentimentTrackerResult {
      const { params, fetchResult, totalMEPs, groupSentiments, doceo, hasDoceoCoverage } = args;
      const validSentiments = groupSentiments.filter(g => g.memberCount > 0);
      const sentimentScores = validSentiments.map(g => g.sentimentScore);
      const overallSentiment = sentimentScores.length > 0
        ? round2(sentimentScores.reduce((s, v) => s + v, 0) / sentimentScores.length)
        : 0;
      const polarizationIndex = computePolarizationIndex(validSentiments, totalMEPs, hasDoceoCoverage, doceo);

      return {
        timeframe: params.timeframe,
        groupSentiments: groupSentiments.map(({ _rawCohesion: _, ...rest }) => rest),
        polarizationIndex,
        consensusTopics: hasDoceoCoverage ? doceo.consensusTopics : [],
        divisiveTopics: hasDoceoCoverage ? doceo.divisiveTopics : [],
        sentimentShifts: buildSentimentShifts(validSentiments, hasDoceoCoverage),
        overallParliamentSentiment: overallSentiment,
        computedAttributes: buildSentimentComputedAttrs(validSentiments, sentimentScores, overallSentiment),
        dataAvailable: validSentiments.length > 0,
        confidenceLevel: determineConfidenceLevel(doceo.rcvVoteCount),
        dataFreshness: hasDoceoCoverage
          ? `EP API MEP composition + DOCEO RCV XML in last ${String(TIMEFRAME_CONFIG[params.timeframe].days)}d window`
          : 'EP API MEP composition only — DOCEO RCV coverage insufficient for this window',
        sourceAttribution: 'European Parliament Open Data Portal - data.europarl.europa.eu',
        methodology: buildMethodology(params.timeframe, hasDoceoCoverage, doceo.rcvVoteCount),
        dataQualityWarnings: buildDataQualityWarnings(
          hasDoceoCoverage,
          doceo.rcvVoteCount,
          fetchResult.complete,
          fetchResult.failureOffset
        ),
      };
    }

    /**
     * Compute political-group sentiment scores over a configurable time window.
     *
     * Implementation of the MCP `sentiment_tracker` tool. Combines current EP API
     * MEP composition with DOCEO roll-call vote (RCV) records inside the requested
     * `timeframe` window to derive a per-group sentiment score, trend, volatility,
     * polarization index, and consensus/divisive topic lists. Falls back to a
     * seat-share-only proxy with `confidenceLevel: 'LOW'` and an explicit
     * `methodology` note when DOCEO coverage is insufficient.
     *
     * @param params - Validated tool parameters (see {@link SentimentTrackerSchema})
     * @returns A {@link ToolResult} containing the sentiment report or a structured
     *   error response on failure.
     */
    export async function sentimentTracker(params: SentimentTrackerParams): Promise<ToolResult> {
      try {
        const fetchResult = await fetchAllCurrentMEPs();
        const allMeps = fetchResult.meps;
        const totalMEPs = allMeps.length;

        if (totalMEPs === 0) {
          return buildToolResponse(buildEmptySentimentResult(params));
        }

        const groupCounts = tallyGroupCounts(allMeps);
        const targetGroups = resolveTargetGroups(params.groupId);
        const doceo = await loadDoceoWindowAggregate(params.timeframe, targetGroups);
        const hasDoceoCoverage = !doceo.empty && doceo.rcvVoteCount >= MIN_RCV_VOTES_FOR_MEDIUM;
        const groupSentiments = buildSentimentScores(targetGroups, {
          groupCounts, totalMEPs, doceo, hasDoceoCoverage,
        });

        return buildToolResponse(
          buildSentimentResult({ params, fetchResult, totalMEPs, groupSentiments, doceo, hasDoceoCoverage })
        );
      } catch (error: unknown) {
        return buildErrorResponse(
          error instanceof Error ? error : new Error(String(error)),
          'sentiment_tracker'
        );
      }
    }

    /**
     * MCP tool metadata for `sentiment_tracker` (name, description, and
     * JSON Schema for the tool's input). Consumed by the server's tool
     * registry to advertise this tool in `ListTools` responses.
     */
    export const sentimentTrackerToolMetadata = {
      name: 'sentiment_tracker',
      description: 'Track political group sentiment over a configurable time window. Combines current EP API MEP composition with DOCEO roll-call vote (RCV) cohesion / defection data aggregated across last_month (~30d), last_quarter (~90d) or last_year (~365d). Returns per-group sentiment scores (-1 to +1), IMPROVING/STABLE/DECLINING/VOLATILE trends derived from half-window and sub-window DOCEO cohesion deltas, polarization index (1 − seat-share-weighted mean cohesion), and consensus/divisive vote subjects. Falls back to seat-share proxy with confidenceLevel=LOW when DOCEO coverage is insufficient.',
      inputSchema: {
        type: 'object' as const,
        properties: {
          groupId: {
            type: 'string',
            description: 'Political group ID to track (omit for all groups)',
            minLength: 1,
            maxLength: 50
          },
          timeframe: {
            type: 'string',
            enum: ['last_month', 'last_quarter', 'last_year'],
            description: 'DOCEO RCV aggregation window: last_month (~30d), last_quarter (~90d), or last_year (~365d). Falls back to seat-share-only proxy with confidenceLevel=LOW when DOCEO coverage is insufficient.',
            default: 'last_quarter'
          },
        }
      }
    };

    /**
     * MCP `CallTool` handler entry point for `sentiment_tracker`.
     *
     * Validates the raw input arguments against {@link SentimentTrackerSchema}
     * and delegates execution to {@link sentimentTracker}.
     *
     * @param args - Raw, untrusted MCP `CallTool` arguments
     * @returns The same {@link ToolResult} produced by {@link sentimentTracker}
     */
    export async function handleSentimentTracker(args: unknown): Promise<ToolResult> {
      const params = SentimentTrackerSchema.parse(args);
      return sentimentTracker(params);
    }