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* MCP Tool: detect_voting_anomalies
*
* Flag unusual voting patterns — party defections, sudden alignment shifts,
* abstention spikes, and behavioral anomalies.
*
* **Intelligence Perspective:** Anomaly detection tool identifying deviations from
* expected voting behavior—enables early warning for party splits, political
* realignments, and emerging cross-party movements.
*
* ISMS Policy: SC-002 (Input Validation), AC-003 (Least Privilege)
*/
import { DetectVotingAnomaliesSchema } from '../schemas/europeanParliament.js';
import { epClient } from '../clients/europeanParliamentClient.js';
import { buildToolResponse } from './shared/responseBuilder.js';
import type { ToolResult } from './shared/types.js';
interface VotingAnomaly {
type: string;
severity: string;
mepId: string;
mepName: string;
description: string;
metrics: { expectedValue: number; actualValue: number; deviation: number };
detectedDate: 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: string;
dataFreshness: string;
sourceAttribution: string;
methodology: string;
}
/**
* Classify attendance severity
*/
function classifyAttendanceSeverity(rate: number): string {
return rate < 50 ? 'HIGH' : 'MEDIUM';
}
/**
* Classify abstention severity
*/
function classifyAbstentionSeverity(rate: number): string {
return rate > 30 ? 'HIGH' : 'MEDIUM';
}
/**
* Classify defection severity
*/
function classifyDefectionSeverity(rate: number): string {
Eif (rate > 40) return 'HIGH';
if (rate > 25) return 'MEDIUM';
return 'LOW';
}
/**
* Classify defection trend
*/
function classifyDefectionTrend(highCount: number): string {
Iif (highCount > 2) return 'INCREASING';
if (highCount > 0) return 'STABLE';
return 'DECREASING';
}
/**
* Classify risk level
*/
function classifyRiskLevel(highCount: number): string {
Iif (highCount > 3) return 'CRITICAL';
Iif (highCount > 1) return 'ELEVATED';
if (highCount > 0) return 'MODERATE';
return 'LOW';
}
/**
* Determine confidence based on data volume, not anomaly count
*/
function getDataVolumeConfidence(scope: string, isSingleMep: boolean): string {
Eif (isSingleMep) return 'MEDIUM';
if (scope === 'All MEPs') return 'HIGH';
return 'MEDIUM';
}
/**
* Check for low attendance anomaly
*/
function checkAttendanceAnomaly(
mep: { id: string; name: string; politicalGroup: string },
stats: { attendanceRate: number },
threshold: number,
detectedDate: string
): VotingAnomaly | undefined {
const expectedAttendance = Math.round((1 - threshold) * 100 * 100) / 100;
if (stats.attendanceRate < expectedAttendance) {
return {
type: 'LOW_ATTENDANCE',
severity: classifyAttendanceSeverity(stats.attendanceRate),
mepId: mep.id,
mepName: mep.name,
description: `Attendance rate ${String(stats.attendanceRate)}% below expected threshold of ${String(expectedAttendance)}% for ${mep.politicalGroup}`,
metrics: { expectedValue: expectedAttendance, actualValue: stats.attendanceRate, deviation: Math.round((expectedAttendance - stats.attendanceRate) * 100) / 100 },
detectedDate
};
}
return undefined;
}
/**
* Check for high abstention anomaly
*/
function checkAbstentionAnomaly(
mep: { id: string; name: string },
stats: { abstentions: number; totalVotes: number },
threshold: number,
detectedDate: string
): VotingAnomaly | undefined {
Iif (stats.totalVotes === 0) return undefined;
const rate = (stats.abstentions / stats.totalVotes) * 100;
Iif (rate > threshold * 50) {
return {
type: 'ABSTENTION_SPIKE',
severity: classifyAbstentionSeverity(rate),
mepId: mep.id,
mepName: mep.name,
description: `Abstention rate of ${String(Math.round(rate))}% significantly above group average`,
metrics: { expectedValue: 10, actualValue: Math.round(rate * 100) / 100, deviation: Math.round((rate - 10) * 100) / 100 },
detectedDate
};
}
return undefined;
}
/**
* Check for party defection anomaly
*/
function checkDefectionAnomaly(
mep: { id: string; name: string; politicalGroup: string },
stats: { votesFor: number; votesAgainst: number },
threshold: number,
detectedDate: string
): VotingAnomaly | undefined {
const decisive = stats.votesFor + stats.votesAgainst;
const rate = decisive > 0 ? (stats.votesAgainst / decisive) * 100 : 0;
if (rate > threshold * 60) {
return {
type: 'PARTY_DEFECTION',
severity: classifyDefectionSeverity(rate),
mepId: mep.id,
mepName: mep.name,
description: `Defection rate of ${String(Math.round(rate))}% — voting against ${mep.politicalGroup} party line`,
metrics: { expectedValue: 10, actualValue: Math.round(rate * 100) / 100, deviation: Math.round((rate - 10) * 100) / 100 },
detectedDate
};
}
return undefined;
}
/**
* Detect anomalies from voting statistics
*/
function detectMepAnomalies(
mep: { id: string; name: string; politicalGroup: string },
stats: { totalVotes: number; votesFor: number; votesAgainst: number; abstentions: number; attendanceRate: number } | undefined,
threshold: number,
detectedDate: string
): VotingAnomaly[] {
Iif (stats === undefined || stats.totalVotes === 0) return [];
const anomalies: VotingAnomaly[] = [];
const att = checkAttendanceAnomaly(mep, stats, threshold, detectedDate);
if (att !== undefined) anomalies.push(att);
const abs = checkAbstentionAnomaly(mep, stats, threshold, detectedDate);
Iif (abs !== undefined) anomalies.push(abs);
const def = checkDefectionAnomaly(mep, stats, threshold, detectedDate);
if (def !== undefined) anomalies.push(def);
return anomalies;
}
/**
* Detect anomalies for a single MEP
*/
async function detectSingleMepAnomalies(
mepId: string, threshold: number, period: { from: string; to: string }
): Promise<{ scope: string; anomalies: VotingAnomaly[]; dataAvailable: boolean }> {
const mep = await epClient.getMEPDetails(mepId);
const mepStats = mep.votingStatistics;
const dataAvailable = mepStats !== undefined && mepStats.totalVotes > 0;
const anomalies = detectMepAnomalies(
{ id: mep.id, name: mep.name, politicalGroup: mep.politicalGroup },
mepStats,
threshold,
period.to
);
return { scope: `MEP: ${mepId}`, anomalies, dataAvailable };
}
/**
* Detect anomalies for a group or all MEPs.
* Note: The EP API /meps/{id} endpoint does not provide per-MEP voting
* statistics, so group-level anomaly detection reports data unavailability
* with LOW confidence rather than fabricating values.
*/
async function detectGroupAnomalies(
groupId: string | undefined, _threshold: number, _period: { from: string; to: string }
): Promise<{ scope: string; anomalies: VotingAnomaly[]; mepCount: number }> {
const groupFilter: { group?: string } = {};
if (groupId !== undefined) {
groupFilter.group = groupId;
}
const scope = groupId !== undefined ? `Group: ${groupId}` : 'All MEPs';
const mepsResult = await epClient.getMEPs({ ...groupFilter, limit: 50 });
// Per-MEP voting statistics are not available from the EP API /meps/{id}
// endpoint, so we cannot detect group-wide anomalies from individual stats.
return { scope, anomalies: [], mepCount: mepsResult.data.length };
}
/**
* Build the anomaly analysis result object from anomaly detection results.
*/
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;
// anomalyRate: true proportion of HIGH-severity anomalies in [0, 1]
const anomalyRate = anomalies.length > 0
? Math.round((highSeverity / anomalies.length) * 100) / 100
: 0;
// severityIndex: weighted-average severity on ~1–3 scale
const severityIndex = anomalies.length > 0
? Math.round((highSeverity * 3 + mediumSeverity * 2 + lowSeverity) / anomalies.length * 100) / 100
: 0;
return { highSeverity, mediumSeverity, lowSeverity, anomalyRate, severityIndex };
}
/**
* Resolve confidence level: MEDIUM when a single MEP has detectable anomalies
* (indicating voting data was available and yielded results); LOW otherwise.
*/
function resolveConfidence(isSingleMep: boolean, scope: string, anomalyCount: number): string {
if (isSingleMep && anomalyCount > 0) {
return getDataVolumeConfidence(scope, true);
}
return 'LOW';
}
/**
* Handles the detect_voting_anomalies MCP tool request.
*
* Detects statistically unusual voting patterns for individual MEPs or entire
* political groups, including cross-party defections, unusual abstention clusters,
* and discipline breakdowns. Returns anomaly records graded by severity with a
* group stability score and defection trend assessment.
*
* @param args - Raw tool arguments, validated against {@link DetectVotingAnomaliesSchema}
* @returns MCP tool result containing detected anomalies with severity ratings,
* summary statistics, anomaly rate, severity index, and risk level classification
* @throws - If `args` fails schema validation (e.g., missing required fields or invalid format)
* - If the European Parliament API is unreachable or returns an error response
*
* @example
* ```typescript
* const result = await handleDetectVotingAnomalies({
* mepId: '124810',
* sensitivityThreshold: 0.7,
* dateFrom: '2024-01-01',
* dateTo: '2024-12-31'
* });
* // Returns anomaly list with severity ratings (HIGH/MEDIUM/LOW),
* // anomaly rate, severity index, and group stability score
* ```
*
* @security - Input is validated with Zod before any API call.
* - Personal data in responses is minimised per GDPR Article 5(1)(c).
* - All requests are rate-limited and audit-logged per ISMS Policy AU-002.
* @since 0.8.0
* @see {@link detectVotingAnomaliesToolMetadata} for MCP schema registration
* @see {@link handleAnalyzeCoalitionDynamics} for coalition-level cohesion and stress analysis
*/
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 mepId = params.mepId;
const isSingleMep = mepId !== undefined;
const result = mepId !== undefined
? await detectSingleMepAnomalies(mepId, params.sensitivityThreshold, period)
: await detectGroupAnomalies(params.groupId, params.sensitivityThreshold, period);
// When single MEP has no voting data, report unavailability with same shape as normal path
Iif (isSingleMep && 'dataAvailable' in result && !result.dataAvailable) {
return buildToolResponse({
period,
targetScope: mepId,
anomalies: [],
summary: { totalAnomalies: 0, highSeverity: 0, mediumSeverity: 0, lowSeverity: 0 },
computedAttributes: {
anomalyRate: 0,
severityIndex: 0,
groupStabilityScore: 0,
defectionTrend: 'UNKNOWN',
riskLevel: 'UNKNOWN'
},
dataAvailable: false,
confidenceLevel: 'LOW',
dataFreshness: 'EP API data — voting statistics unavailable for this MEP',
sourceAttribution: 'European Parliament Open Data Portal - data.europarl.europa.eu',
methodology: 'The EP API /meps/{id} endpoint does not return voting statistics. '
+ 'Anomaly detection requires voting data which is unavailable for this MEP. '
+ 'Data source: European Parliament Open Data Portal.'
});
}
const { highSeverity, mediumSeverity, lowSeverity, anomalyRate, severityIndex } = buildAnomalySummary(result.anomalies);
const confidence = resolveConfidence(isSingleMep, result.scope, result.anomalies.length);
const analysis: VotingAnomalyAnalysis = {
period,
targetScope: result.scope,
anomalies: result.anomalies,
summary: { totalAnomalies: result.anomalies.length, highSeverity, mediumSeverity, lowSeverity },
computedAttributes: {
anomalyRate,
severityIndex,
groupStabilityScore: Math.round((1 - severityIndex / 3) * 100 * 100) / 100,
defectionTrend: classifyDefectionTrend(highSeverity),
riskLevel: classifyRiskLevel(highSeverity)
},
confidenceLevel: confidence,
dataFreshness: 'Real-time EP API data — voting statistics from MEP records',
sourceAttribution: 'European Parliament Open Data Portal - data.europarl.europa.eu',
methodology: 'Heuristic statistical analysis using aggregated voting statistics and MEP metadata '
+ 'from /meps/{id} on the European Parliament Open Data API. Vote-level records are not fetched; '
+ 'many voting statistic fields may be zero or unavailable from the EP API. Group-level analysis '
+ 'reflects data availability — detected anomalies are approximate and indicative only. '
+ 'Data source: European Parliament Open Data Portal (MEP metadata endpoints).'
};
return buildToolResponse(analysis);
} catch (error) {
const errorMessage = error instanceof Error ? error.message : 'Unknown error';
throw new Error(`Failed to detect voting anomalies: ${errorMessage}`);
}
}
/**
* Tool metadata for MCP registration
*/
export const detectVotingAnomaliesToolMetadata = {
name: 'detect_voting_anomalies',
description: 'Detect unusual voting patterns including party defections, abstention spikes, and low attendance anomalies. Configurable sensitivity threshold with severity classification (HIGH/MEDIUM/LOW). Returns anomaly details, group stability score, defection trend, and risk level assessment.',
inputSchema: {
type: 'object' as const,
properties: {
mepId: {
type: 'string',
description: 'MEP identifier (omit for broad analysis)',
minLength: 1,
maxLength: 100
},
groupId: {
type: 'string',
description: 'Political group to analyze',
minLength: 1,
maxLength: 50
},
dateFrom: {
type: 'string',
description: 'Analysis start date (YYYY-MM-DD format)',
pattern: '^\\d{4}-\\d{2}-\\d{2}$'
},
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)',
minimum: 0,
maximum: 1,
default: 0.3
}
}
}
};
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