# All files / src/tools detectVotingAnomalies.ts

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     /**
     * MCP Tool: detect_voting_anomalies
     *
     * Flag unusual voting patterns — party defections, sudden alignment shifts,
     * abstention spikes, and cross-party movement signals — using authoritative
     * DOCEO roll-call (RCV) records.
     *
     * **Intelligence Perspective:** Anomaly detection tool identifying deviations
     * from each MEP's *own* historical voting baseline within the requested
     * period. Surfaces early warning signals for party splits, political
     * realignments, and emerging cross-party movements. Consumed by
     * `correlate_intelligence` and `early_warning_system`.
     *
     * **Data source:** EP DOCEO XML (`PV-{term}-{date}-RCV_{lang}.xml`) via the
     * shared bounded/cached aggregator. Falls back gracefully to LOW confidence
     * with `dataQualityWarnings` when DOCEO is unreachable.
     *
     * ISMS Policy: SC-002 (Input Validation), AC-003 (Least Privilege),
     *   AU-002 (Audit Logging). GDPR Article 5(1)(c)/(d) — minimisation & accuracy.
     *
     * @since 0.8.0
     */

    import { DetectVotingAnomaliesSchema } from '../schemas/europeanParliament.js';
    import { epClient } from '../clients/europeanParliamentClient.js';
    import { doceoClient } from '../clients/ep/doceoClient.js';
    import { auditLogger, toErrorMessage } from '../utils/auditLogger.js';
    import { normalizePoliticalGroup } from '../utils/politicalGroupNormalization.js';
    import { withTimeoutAndAbort } from '../utils/timeout.js';
    import {
      bucketByWeek,
      classifyMepVote,
      coverageConfidence,
      findCrossPartyAlignmentWindows,
      findOutlierWeeks,
      findWoWShifts,
      DEFAULT_CROSS_PARTY_SHARE,
      DEFAULT_WOW_THRESHOLD_PP,
      DEFAULT_Z_SCORE_THRESHOLD,
    } from '../utils/votingBaseline.js';
    import { buildToolResponse } from './shared/responseBuilder.js';
    import type { ToolResult } from './shared/types.js';
    import type { LatestVoteRecord } from '../clients/ep/doceoXmlParser.js';
    import type { ClassifiedVote, WeekBucket } from '../utils/votingBaseline.js';

    /** Max DOCEO RCV records aggregated per request (2 pages × 100 per page). */
    const DOCEO_RCV_FETCH_LIMIT = 200;

    /** Per-page limit for DOCEO getLatestVotes (API enforces ≤ 100). */
    const DOCEO_PAGE_LIMIT = 100;

    /** DOCEO call timeout, mirrors the shared aggregator guard. */
    const DOCEO_TIMEOUT_MS = 2_000;

    /** Cap MEPs inspected per group/all-MEPs request to bound runtime. */
    const MAX_MEPS_PER_REQUEST = 50;

    /** Window (hours) for the NEAR_REALTIME freshness label. */
    const NEAR_REALTIME_WINDOW_HOURS = 72;

    /**
     * Voting anomaly surface, preserves the schema consumed by
     * `correlate_intelligence` and adds `evidenceVoteIds` for traceability.
     */
    interface VotingAnomaly {
      type:
        | 'PARTY_DEFECTION'
        | 'ABSTENTION_SPIKE'
        | 'ALIGNMENT_SHIFT'
        | 'CROSS_PARTY_ALIGNMENT_SHIFT';
      severity: 'HIGH' | 'MEDIUM' | 'LOW';
      mepId: string;
      mepName: string;
      description: string;
      metrics: { expectedValue: number; actualValue: number; deviation: number };
      detectedDate: string;
      /**
       * DOCEO `LatestVoteRecord.id` values supporting the anomaly.
       * These are the unique vote identifiers returned by `get_latest_votes`
       * (e.g. `"RCV-10-2026-01-15-001"`).
       */
      evidenceVoteIds: string[];
    }

    interface VotingAnomalyAnalysis {
      period: { from: string; to: string };
      targetScope: string;
      anomalies: VotingAnomaly[];
      summary: { totalAnomalies: number; highSeverity: number; mediumSeverity: number; lowSeverity: number };
      computedAttributes: {
        anomalyRate: number;
        severityIndex: number;
        groupStabilityScore: number;
        defectionTrend: string;
        riskLevel: string;
      };
      confidenceLevel: 'HIGH' | 'MEDIUM' | 'LOW';
      /**
       * Source of voting records consumed. Always `'DOCEO'` for this tool —
       * matches the closed set used by sibling OSINT tools (`'EP_API' | 'DOCEO'
       * | 'EP_API+DOCEO'`). Use {@link dataAvailable} = `false` to discriminate
       * the "DOCEO unreachable / empty period" branch.
       */
      dataSource: 'DOCEO';
      dataFreshness: string;
      sourceAttribution: string;
      methodology: string;
      dataQualityWarnings: string[];
      /**
       * Minimum per-MEP RCV votes inspected across the analysed scope (worst-case
       * coverage). Drives {@link confidenceLevel} so that group/all-MEP scopes
       * are not inflated by a single MEP with high coverage.
       */
      rcvVotesInspected: number;
      /**
       * Maximum per-MEP RCV votes inspected across the analysed scope (best-case
       * coverage). Surfaced for observability so callers can see the spread
       * between the worst- and best-covered MEP.
       */
      rcvVotesInspectedMax: number;
      mepsAnalyzed: number;
      /**
       * `false` when DOCEO was unreachable or returned no records for the
       * requested period; absent / `true` otherwise. Replaces the previous
       * `dataSource: 'NONE'` sentinel so the `dataSource` field stays a closed
       * set across OSINT tools.
       */
      dataAvailable?: boolean;
    }

    /** Trend assessment derived from high-severity counts. */
    function classifyDefectionTrend(highCount: number): string {
      Iif (highCount > 2) return 'INCREASING';
      if (highCount > 0) return 'STABLE';
      return 'DECREASING';
    }

    /** Risk-level classifier derived from high-severity counts. */
    function classifyRiskLevel(highCount: number): string {
      Iif (highCount > 3) return 'CRITICAL';
      if (highCount > 1) return 'ELEVATED';
      if (highCount > 0) return 'MODERATE';
      return 'LOW';
    }

    /** Map z-score magnitude to severity (preserves prior HIGH/MEDIUM/LOW semantics). */
    function severityFromZ(z: number): 'HIGH' | 'MEDIUM' | 'LOW' {
      Iif (z >= 3) return 'HIGH';
      Iif (z >= 2) return 'MEDIUM';
      return 'LOW';
    }

    /** Map percentage-point deltas to severity. */
    function severityFromDelta(delta: number): 'HIGH' | 'MEDIUM' | 'LOW' {
      if (delta >= 40) return 'HIGH';
      Iif (delta >= 30) return 'MEDIUM';
      return 'LOW';
    }

    /** Map cross-party alignment share to severity. */
    function severityFromShare(share: number): 'HIGH' | 'MEDIUM' | 'LOW' {
      Eif (share >= 90) return 'HIGH';
      if (share >= 75) return 'MEDIUM';
      return 'LOW';
    }

    /**
     * Build the human-readable methodology string emitted alongside each
     * response. Reports the *derived* thresholds actually applied for the
     * request so callers that override `sensitivityThreshold` see the scaled
     * values (not the spec defaults).
     *
     * @internal
     */
    function buildMethodologyDescription(
      thresholds: SensitivityThresholds,
      sensitivity: number
    ): string {
      const z = Math.round(thresholds.zScore * 100) / 100;
      const wow = Math.round(thresholds.wowDelta * 100) / 100;
      const crossPct = Math.round(thresholds.crossPartyShare * 100);
      const isDefault = sensitivity === 0.3;
      const scalingNote = isDefault
        ? '(spec defaults)'
        : `(scaled from spec defaults — z≥${String(DEFAULT_Z_SCORE_THRESHOLD)}, WoW≥`
          + `${String(DEFAULT_WOW_THRESHOLD_PP)}pp, cross-party≥`
          + `${String(Math.round(DEFAULT_CROSS_PARTY_SHARE * 100))}% — `
          + `via sensitivityThreshold=${String(sensitivity)})`;
      return 'Per-MEP defection / abstention / cross-party alignment anomaly detection on DOCEO RCV '
        + 'records (up to 200 via 2×100 paged fetching). Per-vote group majority resolved by plurality with alphabetical '
        + 'tie-breaking. Each MEP vote classified as aligned / defected / abstained / absent against their '
        + `home-group majority. Anomalies emitted when defection-rate z-score ≥ ${String(z)}, `
        + `abstention-rate z-score ≥ ${String(z)}, week-over-week defection delta ≥ ${String(wow)}pp, `
        + `or non-home-group alignment share ≥ ${String(crossPct)}% in a weekly sub-window `
        + `${scalingNote}. Confidence reflects worst-case RCV coverage across analysed MEPs: `
        + 'HIGH ≥50 votes inspected, MEDIUM 10–49, LOW <10. Source: EP DOCEO XML.';
    }

    interface MepRef {
      id: string;
      name: string;
      politicalGroup: string;
    }

    /**
     * Per-request thresholds derived from `sensitivityThreshold`.
     *
     * Defaults match the issue specification: z ≥ 1.5, WoW ≥ 20pp, cross-party
     * share ≥ 0.6. The sensitivity parameter scales these so lower values yield
     * more anomalies (backward-compatible with prior callers).
     */
    interface SensitivityThresholds {
      zScore: number;
      wowDelta: number;
      crossPartyShare: number;
    }

    function deriveThresholds(sensitivity: number): SensitivityThresholds {
      // Linear scale anchored at the default 0.3 → spec defaults.
      const factor = sensitivity / 0.3;
      return {
        zScore: Math.max(0.5, DEFAULT_Z_SCORE_THRESHOLD * factor),
        wowDelta: Math.max(5, DEFAULT_WOW_THRESHOLD_PP * factor),
        crossPartyShare: Math.min(0.95, Math.max(0.3, DEFAULT_CROSS_PARTY_SHARE * factor)),
      };
    }

    /**
     * Detect anomalies for a single MEP from the supplied DOCEO RCV records.
     *
     * @param mep - MEP reference (id/name/politicalGroup).
     * @param votes - All DOCEO RCV records in the inspection window.
     * @param thresholds - Sensitivity-adjusted detection thresholds.
     * @param detectedDate - Period end date stamped onto anomaly records.
     * @returns Detected anomalies and the per-MEP RCV inspection count.
     */
    function detectMepAnomaliesFromDoceo(
      mep: MepRef,
      votes: LatestVoteRecord[],
      thresholds: SensitivityThresholds,
      detectedDate: string
    ): { anomalies: VotingAnomaly[]; classified: ClassifiedVote[]; buckets: WeekBucket[] } {
      const homeGroup = mep.politicalGroup !== '' ? normalizePoliticalGroup(mep.politicalGroup) : null;
      const classified = votes.map(v => classifyMepVote(v, mep.id, homeGroup));
      const buckets = bucketByWeek(classified);
      const anomalies: VotingAnomaly[] = [];

      for (const outlier of findOutlierWeeks(buckets, 'defectionRate', thresholds.zScore)) {
        anomalies.push({
          type: 'PARTY_DEFECTION',
          severity: severityFromZ(outlier.z),
          mepId: mep.id,
          mepName: mep.name,
          description: `Defection rate of ${String(outlier.value)}% during week of ${outlier.weekStart} `
            + `(z=${String(outlier.z)} vs ${mep.politicalGroup || 'home group'} majority on DOCEO RCVs)`,
          metrics: {
            expectedValue: outlier.baselineMean,
            actualValue: outlier.value,
            deviation: Math.round((outlier.value - outlier.baselineMean) * 100) / 100,
          },
          detectedDate,
          evidenceVoteIds: outlier.voteIds,
        });
      }

      for (const outlier of findOutlierWeeks(buckets, 'abstentionRate', thresholds.zScore)) {
        anomalies.push({
          type: 'ABSTENTION_SPIKE',
          severity: severityFromZ(outlier.z),
          mepId: mep.id,
          mepName: mep.name,
          description: `Abstention rate of ${String(outlier.value)}% during week of ${outlier.weekStart} `
            + `(z=${String(outlier.z)} vs MEP's own baseline)`,
          metrics: {
            expectedValue: outlier.baselineMean,
            actualValue: outlier.value,
            deviation: Math.round((outlier.value - outlier.baselineMean) * 100) / 100,
          },
          detectedDate,
          evidenceVoteIds: outlier.voteIds,
        });
      }

      for (const shift of findWoWShifts(buckets, thresholds.wowDelta)) {
        anomalies.push({
          type: 'ALIGNMENT_SHIFT',
          severity: severityFromDelta(shift.delta),
          mepId: mep.id,
          mepName: mep.name,
          description: `Week-over-week defection rate jumped by ${String(shift.delta)}pp `
            + `(${shift.fromWeek} → ${shift.toWeek})`,
          metrics: {
            expectedValue: shift.previousRate,
            actualValue: Math.round((shift.previousRate + shift.delta) * 100) / 100,
            deviation: shift.delta,
          },
          detectedDate,
          evidenceVoteIds: shift.voteIds,
        });
      }

      for (const window of findCrossPartyAlignmentWindows(buckets, thresholds.crossPartyShare)) {
        const thresholdPct = Math.round(thresholds.crossPartyShare * 100);
        anomalies.push({
          type: 'CROSS_PARTY_ALIGNMENT_SHIFT',
          severity: severityFromShare(window.sharePercent),
          mepId: mep.id,
          mepName: mep.name,
          description: `Voted with non-home group majorities on ${String(window.sharePercent)}% of `
            + `${String(window.decisive)} decisive RCVs during week of ${window.weekStart}`,
          metrics: {
            expectedValue: thresholdPct,
            actualValue: window.sharePercent,
            deviation: Math.round((window.sharePercent - thresholdPct) * 100) / 100,
          },
          detectedDate,
          evidenceVoteIds: window.voteIds,
        });
      }

      return { anomalies, classified, buckets };
    }

    /** Count RCV votes where the MEP actually appeared on the roll. */
    function countVotesInspected(classified: ClassifiedVote[]): number {
      let n = 0;
      for (const c of classified) Eif (c.alignment !== 'absent') n += 1;
      return n;
    }

    /**
     * Determine whether the DOCEO source can be labelled NEAR_REALTIME.
     *
     * NEAR_REALTIME requires at least one DOCEO record with a sitting date within
     * the configured window (default 72h) of `referenceDate`.
     */
    function isNearRealtime(votes: LatestVoteRecord[], referenceDate: Date): boolean {
      const cutoff = referenceDate.getTime() - NEAR_REALTIME_WINDOW_HOURS * 3_600_000;
      for (const v of votes) {
        const date = v.sittingDate ?? v.date;
        Iif (date === '') continue;
        const ts = Date.parse(date);
        Iif (!Number.isNaN(ts) && ts >= cutoff) return true;
      }
      return false;
    }

    /**
     * Return the most recent valid sitting date in the supplied record list,
     * or `null` when none of the records have a parseable date.
     *
     * @internal
     */
    function mostRecentSittingDate(votes: LatestVoteRecord[]): Date | null {
      let latest: number | null = null;
      for (const v of votes) {
        const date = v.sittingDate ?? v.date;
        Iif (date === '') continue;
        const ts = Date.parse(date);
        Iif (Number.isNaN(ts)) continue;
        if (latest === null || ts > latest) latest = ts;
      }
      return latest === null ? null : new Date(latest);
    }

    /**
     * Fetch DOCEO RCV records covering the request window. Implements paged
     * fetching (2 × 100) since the DOCEO client validates `limit ≤ 100`.
     * Returns an empty sentinel when DOCEO is unreachable so the caller can
     * surface a graceful `dataQualityWarning` instead of throwing.
     */
    async function fetchDoceoRcvRecords(
      period: { from: string; to: string }
    ): Promise<{ records: LatestVoteRecord[]; doceoAvailable: boolean }> {
      try {
        // Normalize period.to to an ISO date string for DOCEO weekStart.
        // DOCEO `weekStart` selects the plenary week containing the supplied date;
        // we use the period's *end* date to capture the most recent week in scope.
        const firstPage = await withTimeoutAndAbort(
          (signal) => doceoClient.getLatestVotes({
            includeIndividualVotes: true,
            limit: DOCEO_PAGE_LIMIT,
            offset: 0,
            weekStart: period.to,
            abortSignal: signal,
          }),
          DOCEO_TIMEOUT_MS,
          'DOCEO RCV fetch (page 1) timed out'
        );
        let allRecords = firstPage.data;

        // Second page if the first was full — up to DOCEO_RCV_FETCH_LIMIT total.
        if (firstPage.data.length >= DOCEO_PAGE_LIMIT) {
          try {
            const secondPage = await withTimeoutAndAbort(
              (signal) => doceoClient.getLatestVotes({
                includeIndividualVotes: true,
                limit: DOCEO_PAGE_LIMIT,
                offset: DOCEO_PAGE_LIMIT,
                weekStart: period.to,
                abortSignal: signal,
              }),
              DOCEO_TIMEOUT_MS,
              'DOCEO RCV fetch (page 2) timed out'
            );
            allRecords = [...allRecords, ...secondPage.data];
          } catch {
            // Second page failure is non-fatal; continue with first page only.
          }
        }

        const records = allRecords
          .filter(v => v.dataSource === 'RCV')
          .slice(0, DOCEO_RCV_FETCH_LIMIT);
        return { records, doceoAvailable: true };
      } catch (error: unknown) {
        auditLogger.logError('detect_voting_anomalies.doceo_fetch', { from: period.from, to: period.to }, toErrorMessage(error));
        return { records: [], doceoAvailable: false };
      }
    }

    interface DetectionResult {
      scope: string;
      anomalies: VotingAnomaly[];
      /** Minimum per-MEP RCV votes inspected (worst-case coverage). */
      rcvVotesInspected: number;
      /** Maximum per-MEP RCV votes inspected (best-case coverage). */
      rcvVotesInspectedMax: number;
      mepsAnalyzed: number;
    }

    /**
     * Aggregate per-MEP anomaly detection over a list of MEPs.
     *
     * Returns *minimum* and *maximum* RCV votes inspected across the MEP list.
     * The minimum drives confidence/warnings so that group/all-MEP scopes are not
     * inflated by a single high-coverage MEP — when most MEPs have few or zero
     * inspected votes (e.g. broad scope, narrow DOCEO window) the response
     * correctly reports LOW/MEDIUM confidence instead of HIGH.
     */
    function runDetectionForMeps(
      meps: MepRef[],
      records: LatestVoteRecord[],
      thresholds: SensitivityThresholds,
      detectedDate: string
    ): { anomalies: VotingAnomaly[]; rcvVotesInspected: number; rcvVotesInspectedMax: number } {
      const allAnomalies: VotingAnomaly[] = [];
      const perMepInspected: number[] = [];
      for (const mep of meps) {
        const { anomalies, classified } = detectMepAnomaliesFromDoceo(mep, records, thresholds, detectedDate);
        const inspected = countVotesInspected(classified);
        perMepInspected.push(inspected);
        allAnomalies.push(...anomalies);
        auditLogger.logDataAccess(
          'detect_voting_anomalies.mep_analysis',
          { mepId: mep.id, rcvVotesInspected: inspected },
          inspected
        );
      }
      // Worst-case coverage drives confidence; best-case is reported separately
      // for observability. When no MEPs were analysed both values are 0.
      const rcvVotesInspected = perMepInspected.length > 0 ? Math.min(...perMepInspected) : 0;
      const rcvVotesInspectedMax = perMepInspected.length > 0 ? Math.max(...perMepInspected) : 0;
      return { anomalies: allAnomalies, rcvVotesInspected, rcvVotesInspectedMax };
    }

    /**
     * Detect anomalies for a single MEP.
     *
     * @internal Exported for unit testing.
     */
    async function detectSingleMepAnomalies(
      mepId: string,
      thresholds: SensitivityThresholds,
      period: { from: string; to: string },
      records: LatestVoteRecord[]
    ): Promise<DetectionResult> {
      const mep = await epClient.getMEPDetails(mepId);
      const { anomalies, rcvVotesInspected, rcvVotesInspectedMax } = runDetectionForMeps(
        [{ id: mep.id, name: mep.name, politicalGroup: mep.politicalGroup }],
        records,
        thresholds,
        period.to
      );
      return { scope: `MEP: ${mepId}`, anomalies, rcvVotesInspected, rcvVotesInspectedMax, mepsAnalyzed: 1 };
    }

    /** Detect anomalies for a political group or all MEPs (bounded scan). */
    async function detectGroupAnomalies(
      groupId: string | undefined,
      thresholds: SensitivityThresholds,
      period: { from: string; to: string },
      records: LatestVoteRecord[]
    ): Promise<DetectionResult> {
      const groupFilter: { group?: string } = {};
      if (groupId !== undefined) groupFilter.group = groupId;
      const scope = groupId !== undefined ? `Group: ${groupId}` : 'All MEPs';
      const mepsResult = await epClient.getCurrentMEPs({ ...groupFilter, limit: MAX_MEPS_PER_REQUEST });
      const meps: MepRef[] = mepsResult.data.map(m => ({
        id: m.id,
        name: m.name,
        politicalGroup: m.politicalGroup,
      }));
      const { anomalies, rcvVotesInspected, rcvVotesInspectedMax } =
        runDetectionForMeps(meps, records, thresholds, period.to);
      return { scope, anomalies, rcvVotesInspected, rcvVotesInspectedMax, mepsAnalyzed: meps.length };
    }

    /** Roll up anomalies into severity counts and derived attributes. */
    function buildAnomalySummary(anomalies: VotingAnomaly[]): {
      highSeverity: number;
      mediumSeverity: number;
      lowSeverity: number;
      anomalyRate: number;
      severityIndex: number;
    } {
      const highSeverity = anomalies.filter(a => a.severity === 'HIGH').length;
      const mediumSeverity = anomalies.filter(a => a.severity === 'MEDIUM').length;
      const lowSeverity = anomalies.filter(a => a.severity === 'LOW').length;
      const anomalyRate = anomalies.length > 0
        ? Math.round((highSeverity / anomalies.length) * 100) / 100
        : 0;
      const severityIndex = anomalies.length > 0
        ? Math.round(((highSeverity * 3 + mediumSeverity * 2 + lowSeverity) / anomalies.length) * 100) / 100
        : 0;
      return { highSeverity, mediumSeverity, lowSeverity, anomalyRate, severityIndex };
    }

    /**
     * Compute data-quality warnings for a given fetch outcome.
     *
     * @internal
     */
    function collectDataQualityWarnings(
      doceoAvailable: boolean,
      recordCount: number,
      rcvVotesInspected: number
    ): string[] {
      const warnings: string[] = [];
      if (!doceoAvailable) {
        warnings.push('DOCEO RCV source unavailable — anomaly detection deferred (LOW confidence).');
      } else if (recordCount === 0) {
        warnings.push('No DOCEO RCV records returned for the requested period.');
      } else if (rcvVotesInspected < 10) {
        warnings.push('Fewer than 10 RCV votes inspected — confidence reduced to LOW.');
      }
      return warnings;
    }

    /**
     * Resolve the data freshness label.
     *
     * - `NEAR_REALTIME` when at least one DOCEO sitting falls within
     *   {@link NEAR_REALTIME_WINDOW_HOURS} of "now".
     * - Otherwise, surface the most recent sitting date and the actual age in
     *   hours so consumers can reason about staleness without inflating the
     *   window.
     */
    function resolveDataFreshness(
      dataAvailable: boolean,
      records: LatestVoteRecord[]
    ): string {
      if (!dataAvailable) return 'No DOCEO RCV data available for the requested period';
      const now = new Date();
      Iif (isNearRealtime(records, now)) return 'NEAR_REALTIME';
      const latest = mostRecentSittingDate(records);
      Iif (latest === null) return `DOCEO RCV data — outside ${String(NEAR_REALTIME_WINDOW_HOURS)}h NEAR_REALTIME window`;
      const ageMs = now.getTime() - latest.getTime();
      const ageHours = Math.max(0, Math.round(ageMs / 3_600_000));
      return `DOCEO RCV data — latest sitting ${String(ageHours)}h old (outside ${String(NEAR_REALTIME_WINDOW_HOURS)}h NEAR_REALTIME window)`;
    }

    /** Resolve the confidence level from the fetch outcome and coverage. */
    function resolveConfidenceLevel(
      dataAvailable: boolean,
      rcvVotesInspected: number
    ): 'HIGH' | 'MEDIUM' | 'LOW' {
      if (!dataAvailable) return 'LOW';
      return coverageConfidence(rcvVotesInspected);
    }

    /**
     * Handles the `detect_voting_anomalies` MCP tool request.
     *
     * See file-level JSDoc for methodology details. Each anomaly carries
     * `evidenceVoteIds` referencing the contributing DOCEO RCV records.
     *
     * @param args - Raw tool arguments, validated against {@link DetectVotingAnomaliesSchema}
     * @returns MCP tool result containing detected anomalies with evidence vote IDs
     * @throws If `args` fails schema validation or the EP API is unreachable.
     *
     * @security Input validated with Zod. Audit logs record only `mepId` and
     *   sanitised counts (no MEP names or vote contents).
     * @since 0.8.0
     */
    export async function handleDetectVotingAnomalies(
      args: unknown
    ): Promise<ToolResult> {
      const params = DetectVotingAnomaliesSchema.parse(args);

      try {
        const period = { from: params.dateFrom ?? '2024-01-01', to: params.dateTo ?? '2024-12-31' };
        const thresholds = deriveThresholds(params.sensitivityThreshold);

        const { records, doceoAvailable } = await fetchDoceoRcvRecords(period);

        const result = params.mepId !== undefined
          ? await detectSingleMepAnomalies(params.mepId, thresholds, period, records)
          : await detectGroupAnomalies(params.groupId, thresholds, period, records);

        const summary = buildAnomalySummary(result.anomalies);
        const dataAvailable = doceoAvailable && records.length > 0;

        const analysis: VotingAnomalyAnalysis = {
          period,
          targetScope: result.scope,
          anomalies: result.anomalies,
          summary: {
            totalAnomalies: result.anomalies.length,
            highSeverity: summary.highSeverity,
            mediumSeverity: summary.mediumSeverity,
            lowSeverity: summary.lowSeverity,
          },
          computedAttributes: {
            anomalyRate: summary.anomalyRate,
            severityIndex: summary.severityIndex,
            groupStabilityScore: Math.round((1 - summary.severityIndex / 3) * 100 * 100) / 100,
            defectionTrend: classifyDefectionTrend(summary.highSeverity),
            riskLevel: classifyRiskLevel(summary.highSeverity),
          },
          confidenceLevel: resolveConfidenceLevel(dataAvailable, result.rcvVotesInspected),
          dataSource: 'DOCEO',
          dataFreshness: resolveDataFreshness(dataAvailable, records),
          sourceAttribution: 'European Parliament DOCEO XML — europarl.europa.eu/doceo',
          methodology: buildMethodologyDescription(thresholds, params.sensitivityThreshold),
          dataQualityWarnings: collectDataQualityWarnings(doceoAvailable, records.length, result.rcvVotesInspected),
          rcvVotesInspected: result.rcvVotesInspected,
          rcvVotesInspectedMax: result.rcvVotesInspectedMax,
          mepsAnalyzed: result.mepsAnalyzed,
        };
        if (!dataAvailable) {
          analysis.dataAvailable = false;
        }

        return buildToolResponse(analysis);
      } catch (error: unknown) {
        const errorMessage = error instanceof Error ? error.message : 'Unknown error';
        throw new Error(`Failed to detect voting anomalies: ${errorMessage}`);
      }
    }

    /**
     * Tool metadata for MCP registration
     */
    export const detectVotingAnomaliesToolMetadata = {
      name: 'detect_voting_anomalies',
      description: 'Detect unusual voting patterns including party defections, abstention spikes, week-over-week alignment shifts, and cross-party movement signals. Uses DOCEO RCV roll-call records and per-MEP rolling baselines (defection z ≥1.5, abstention z ≥1.5, WoW Δ ≥20pp, cross-party share ≥60%). Returns anomalies with evidenceVoteIds, severity classification (HIGH/MEDIUM/LOW), group stability score, defection trend, and risk level.',
      inputSchema: {
        type: 'object' as const,
        properties: {
          mepId: {
            type: 'string',
            description: 'MEP identifier (omit for broad analysis)',
            minLength: 1,
            maxLength: 100
          },
          groupId: {
            type: 'string',
            description: 'Political group to analyze',
            minLength: 1,
            maxLength: 50
          },
          dateFrom: {
            type: 'string',
            description: 'Analysis start date (YYYY-MM-DD format)',
            pattern: '^\\d{4}-\\d{2}-\\d{2}$'
          },
          dateTo: {
            type: 'string',
            description: 'Analysis end date (YYYY-MM-DD format)',
            pattern: '^\\d{4}-\\d{2}-\\d{2}$'
          },
          sensitivityThreshold: {
            type: 'number',
            description: 'Anomaly sensitivity (0-1, lower = more anomalies detected). Default 0.3 matches the spec thresholds (z≥1.5, WoW≥20pp, cross-party≥60%).',
            minimum: 0,
            maximum: 1,
            default: 0.3
          }
        }
      }
    };