AlphaGenome MCP Server

Instructions

Implementation Reference

• src/index.ts:130-143 (handler)

  MCP CallTool request handler case for 'batch_score_variants': validates input using batchScoreSchema, calls AlphaGenomeClient.batchScore(), formats result with formatBatchResult() and returns as text content.
  case 'batch_score_variants': { const params = validateInput(batchScoreSchema, args) as BatchScoreParams; const result = await getClient().batchScore(params); const formatted = formatBatchResult(result); return { content: [ { type: 'text', text: formatted, }, ], };
• src/alphagenome-client.ts:218-233 (handler)

  AlphaGenomeClient.batchScore method: core execution logic that invokes Python bridge with action 'batch_score', passing variants and scoring parameters, handles errors.
  async batchScore(params: BatchScoreParams): Promise<BatchResult> { try { const result = await this.callPythonBridge<BatchResult>('batch_score', { variants: params.variants, scoring_metric: params.scoring_metric, top_n: params.top_n, }); return result; } catch (error) { if (error instanceof ApiError) { throw error; } throw new ApiError(`Batch scoring failed: ${error}`, 500); } }
• src/tools.ts:74-148 (schema)

  Tool object definition for 'batch_score_variants' including name, description, and detailed inputSchema for MCP tool listing and validation.
  export const BATCH_SCORE_TOOL: Tool = { name: 'batch_score_variants', description: `Score and prioritize multiple genetic variants using AlphaGenome AI. Powered by Google DeepMind's AlphaGenome model for high-throughput variant scoring. Analyzes up to 100 variants simultaneously and ranks them by regulatory impact. Scoring metrics: - rna_seq: Gene expression changes - splice: Splicing alterations - regulatory_impact: Combined regulatory score - combined: All metrics weighted Perfect for: GWAS post-analysis, VCF filtering, variant prioritization. Example: "Score these 50 variants and show me the top 10 by regulatory impact"`, inputSchema: { type: 'object', properties: { variants: { type: 'array', items: { type: 'object', properties: { chromosome: { type: 'string', description: 'Chromosome', pattern: '^chr([1-9]|1[0-9]|2[0-2]|X|Y)$', }, position: { type: 'number', description: 'Position', minimum: 1, }, ref: { type: 'string', description: 'Reference allele', pattern: '^[ATGCatgc]+$', }, alt: { type: 'string', description: 'Alternate allele', pattern: '^[ATGCatgc]+$', }, variant_id: { type: 'string', description: 'Optional: variant identifier (e.g., rs number)', }, }, required: ['chromosome', 'position', 'ref', 'alt'], }, description: 'List of variants to analyze (1-100)', minItems: 1, maxItems: 100, }, scoring_metric: { type: 'string', enum: ['rna_seq', 'splice', 'regulatory_impact', 'combined'], description: 'Metric to use for scoring and ranking', }, top_n: { type: 'number', description: 'Number of top variants to return (default: 10, max: 100)', minimum: 1, maximum: 100, }, include_interpretation: { type: 'boolean', description: 'Include detailed clinical interpretation (default: false)', }, }, required: ['variants', 'scoring_metric'], }, };
• src/utils/validation.ts:88-104 (schema)

  Zod schema 'batchScoreSchema' for runtime input validation of batch_score_variants parameters, used in the handler.
  export const batchScoreSchema = z.object({ variants: z .array( z.object({ chromosome: chromosomeSchema, position: positiveIntSchema, ref: nucleotideSchema, alt: nucleotideSchema, variant_id: z.string().optional(), }) ) .min(1, 'At least one variant is required') .max(100, 'Maximum 100 variants allowed'), scoring_metric: z.enum(['rna_seq', 'splice', 'regulatory_impact', 'combined']), top_n: z.number().int().min(1).max(100).optional().default(10), include_interpretation: z.boolean().optional().default(false), });
• src/utils/formatting.ts:213-266 (helper)

  formatBatchResult helper: formats the BatchResult from client into human-readable Markdown output with top variants table, impact distribution, and disclaimers.
  export function formatBatchResult(result: BatchResult): string { const date = new Date().toISOString().split('T')[0]; const time = new Date().toISOString().split('T')[1].split('.')[0]; let output = `# 📊 AlphaGenome Batch Variant Analysis\n\n`; output += `**Total Variants Analyzed**: ${result.total_analyzed}\n`; output += `**Analysis Date**: ${date} ${time} UTC\n\n`; output += `---\n\n`; output += `## 🏆 Top Variants by Impact\n\n`; output += `| Rank | Variant ID | Location | Impact Score | Impact Level | Key Effect |\n`; output += `|------|------------|----------|--------------|--------------|------------|\n`; result.variants.forEach((v) => { const badge = v.impact_level === 'high' ? '🔴 High' : v.impact_level === 'moderate' ? '🟡 Moderate' : '🟢 Low'; const varId = v.variant_id || '-'; const keyEffect = v.key_effect || 'Regulatory element'; output += `| ${v.rank} | ${varId} | ${v.variant} | ${v.score.toFixed( 2 )} | ${badge} | ${keyEffect} |\n`; }); output += `\n---\n\n`; output += `## 📈 Impact Distribution\n\n`; output += `\`\`\`\n`; output += `Impact Level | Count | Percentage\n`; output += `----------------|-------|------------\n`; for (const [level, count] of Object.entries(result.distribution)) { const pct = ((count / result.total_analyzed) * 100).toFixed(1); const emoji = level === 'high' ? '🔴' : level === 'moderate' ? '🟡' : '🟢'; output += `${emoji} ${level.padEnd(13)} | ${String(count).padStart(5)} | ${pct.padStart(5)}%\n`; } output += `----------------|-------|------------\n`; output += `Total | ${String(result.total_analyzed).padStart(5)} | 100.0%\n`; output += `\`\`\`\n\n`; output += `---\n\n`; output += `## ⚠️ Important Disclaimer\n\n`; output += `**For Research Use Only**\n\n`; output += `- ⚠️ NOT for clinical use or diagnosis\n`; output += `- ✅ Powered by AlphaGenome AI predictions\n`; output += `- 📋 Validate with experimental data\n\n`; output += `---\n\n`; output += `*AlphaGenome MCP Server v0.1.0*\n`; output += `*GitHub: https://github.com/taehojo/alphagenome-mcp*\n`; return output; }