AlphaGenome MCP Server

Instructions

Implementation Reference

• src/tools.ts:391-425 (schema)

  Defines the input schema and metadata for the batch_tissue_comparison tool, including validation for variants (array of chromosome, position, ref, alt) and tissues arrays.
  export const BATCH_TISSUE_COMPARISON_TOOL: Tool = { name: 'batch_tissue_comparison', description: `Analyze multiple variants across multiple tissues. Efficient batch analysis of variants × tissues combinations. Perfect for: large-scale tissue-specificity studies. Example: "Test 10 variants in brain, liver, heart"`, inputSchema: { type: 'object', properties: { variants: { type: 'array', items: { type: 'object', properties: { chromosome: { type: 'string' }, position: { type: 'number' }, ref: { type: 'string' }, alt: { type: 'string' }, }, required: ['chromosome', 'position', 'ref', 'alt'], }, minItems: 1, }, tissues: { type: 'array', items: { type: 'string' }, minItems: 1, }, }, required: ['variants', 'tissues'], }, };
• scripts/alphagenome_bridge.py:451-483 (handler)

  Core implementation of batch_tissue_comparison: iterates over variants and tissues, calls predict_variant_effect for each combination, aggregates impact and expression fold-change results.
  def batch_tissue_comparison(client, params: Dict[str, Any]) -> Dict[str, Any]: """ Analyze multiple variants across multiple tissues. """ variants_data = params.get('variants', []) tissues = params.get('tissues', ['brain', 'liver', 'heart']) results = [] for v in variants_data: var_id = f"{v.get('chromosome')}:{v.get('position')}{v.get('ref')}>{v.get('alt')}" tissue_results = {} for tissue in tissues: v['tissue_type'] = tissue try: result = predict_variant_effect(client, v) tissue_results[tissue] = { 'impact': result['interpretation']['impact_level'], 'expression_fc': result['predictions'].get('rna_seq', {}).get('fold_change', 0) } except Exception as e: tissue_results[tissue] = {'error': str(e)} results.append({ 'variant': var_id, 'tissues': tissue_results }) return { 'total_variants': len(results), 'tissues_tested': tissues, 'results': results }
• src/index.ts:198-203 (registration)

  MCP server registration: handles tool calls for batch_tissue_comparison by invoking the AlphaGenome client method.
  case 'batch_tissue_comparison': { const result = await getClient().batchTissueComparison(args); return { content: [{ type: 'text', text: JSON.stringify(result, null, 2) }], }; }
• src/tools.ts:425-719 (registration)

  Tool registration: includes BATCH_TISSUE_COMPARISON_TOOL in the ALL_TOOLS export array for MCP tool discovery.
  }; // Group C Tools: Occasionally Useful export const PREDICT_TF_BINDING_IMPACT_TOOL: Tool = { name: 'predict_tf_binding_impact', description: `Focus on transcription factor binding effects only. Analyzes TF binding site changes using ChIP-seq predictions. Perfect for: TF binding site variants, regulatory element analysis. Example: "Analyze TF binding impact of chr1:12345678G>A"`, inputSchema: { type: 'object', properties: { chromosome: { type: 'string', pattern: '^chr([1-9]|1[0-9]|2[0-2]|X|Y)$' }, position: { type: 'number', minimum: 1 }, ref: { type: 'string', pattern: '^[ATGCatgc]+$' }, alt: { type: 'string', pattern: '^[ATGCatgc]+$' }, tissue_type: { type: 'string' }, }, required: ['chromosome', 'position', 'ref', 'alt'], }, }; export const PREDICT_CHROMATIN_IMPACT_TOOL: Tool = { name: 'predict_chromatin_impact', description: `Focus on chromatin accessibility effects only. Analyzes DNase and ATAC-seq predictions for chromatin state changes. Perfect for: enhancer variants, regulatory region analysis. Example: "Analyze chromatin impact of chr2:23456789C>T"`, inputSchema: { type: 'object', properties: { chromosome: { type: 'string', pattern: '^chr([1-9]|1[0-9]|2[0-2]|X|Y)$' }, position: { type: 'number', minimum: 1 }, ref: { type: 'string', pattern: '^[ATGCatgc]+$' }, alt: { type: 'string', pattern: '^[ATGCatgc]+$' }, tissue_type: { type: 'string' }, }, required: ['chromosome', 'position', 'ref', 'alt'], }, }; export const COMPARE_PROTECTIVE_RISK_TOOL: Tool = { name: 'compare_protective_risk', description: `Compare protective vs risk alleles directly. Side-by-side comparison of alleles with opposite disease associations. Perfect for: disease mechanism studies, therapeutic target identification. Example: "Compare APOE protective allele vs risk allele"`, inputSchema: { type: 'object', properties: { protective_variant: { type: 'object', properties: { chromosome: { type: 'string', pattern: '^chr([1-9]|1[0-9]|2[0-2]|X|Y)$' }, position: { type: 'number', minimum: 1 }, ref: { type: 'string', pattern: '^[ATGCatgc]+$' }, alt: { type: 'string', pattern: '^[ATGCatgc]+$' }, }, required: ['chromosome', 'position', 'ref', 'alt'], }, risk_variant: { type: 'object', properties: { chromosome: { type: 'string', pattern: '^chr([1-9]|1[0-9]|2[0-2]|X|Y)$' }, position: { type: 'number', minimum: 1 }, ref: { type: 'string', pattern: '^[ATGCatgc]+$' }, alt: { type: 'string', pattern: '^[ATGCatgc]+$' }, }, required: ['chromosome', 'position', 'ref', 'alt'], }, }, required: ['protective_variant', 'risk_variant'], }, }; export const BATCH_PATHOGENICITY_FILTER_TOOL: Tool = { name: 'batch_pathogenicity_filter', description: `Filter variants by pathogenicity threshold. Efficiently identifies pathogenic variants from large lists. Perfect for: VCF filtering, prioritizing clinical variants. Example: "Filter 100 variants for pathogenicity > 0.7"`, inputSchema: { type: 'object', properties: { variants: { type: 'array', items: { type: 'object', properties: { chromosome: { type: 'string' }, position: { type: 'number' }, ref: { type: 'string' }, alt: { type: 'string' }, }, required: ['chromosome', 'position', 'ref', 'alt'], }, minItems: 1, }, threshold: { type: 'number', minimum: 0, maximum: 1, description: 'Pathogenicity threshold (0-1, default: 0.5)', }, }, required: ['variants'], }, }; export const COMPARE_VARIANTS_SAME_GENE_TOOL: Tool = { name: 'compare_variants_same_gene', description: `Compare multiple variants within the same gene. Ranks variants by impact within a single gene context. Perfect for: gene-level analysis, compound heterozygote analysis. Example: "Compare 5 BRCA1 variants"`, inputSchema: { type: 'object', properties: { variants: { type: 'array', items: { type: 'object', properties: { chromosome: { type: 'string' }, position: { type: 'number' }, ref: { type: 'string' }, alt: { type: 'string' }, }, required: ['chromosome', 'position', 'ref', 'alt'], }, minItems: 2, }, gene_name: { type: 'string', description: 'Optional: gene name for context', }, }, required: ['variants'], }, }; export const PREDICT_ALLELE_SPECIFIC_EFFECTS_TOOL: Tool = { name: 'predict_allele_specific_effects', description: `Analyze allele-specific regulatory effects. Detailed analysis of how each allele affects gene regulation differently. Perfect for: ASE analysis, imprinting studies. Example: "Analyze allele-specific effects of chr15:67890123A>G"`, inputSchema: { type: 'object', properties: { chromosome: { type: 'string', pattern: '^chr([1-9]|1[0-9]|2[0-2]|X|Y)$' }, position: { type: 'number', minimum: 1 }, ref: { type: 'string', pattern: '^[ATGCatgc]+$' }, alt: { type: 'string', pattern: '^[ATGCatgc]+$' }, tissue_type: { type: 'string' }, }, required: ['chromosome', 'position', 'ref', 'alt'], }, }; export const ANNOTATE_REGULATORY_CONTEXT_TOOL: Tool = { name: 'annotate_regulatory_context', description: `Provide comprehensive regulatory annotation for a variant. Returns detailed regulatory context including all modalities. Perfect for: variant annotation pipelines, comprehensive reports. Example: "Annotate regulatory context of chr7:12345678C>A"`, inputSchema: { type: 'object', properties: { chromosome: { type: 'string', pattern: '^chr([1-9]|1[0-9]|2[0-2]|X|Y)$' }, position: { type: 'number', minimum: 1 }, ref: { type: 'string', pattern: '^[ATGCatgc]+$' }, alt: { type: 'string', pattern: '^[ATGCatgc]+$' }, tissue_type: { type: 'string' }, }, required: ['chromosome', 'position', 'ref', 'alt'], }, }; export const BATCH_MODALITY_SCREEN_TOOL: Tool = { name: 'batch_modality_screen', description: `Screen variants across specific regulatory modalities. Efficiently tests multiple variants for specific regulatory effects. Perfect for: targeted regulatory screens, modality-specific studies. Example: "Screen 20 variants for splicing effects"`, inputSchema: { type: 'object', properties: { variants: { type: 'array', items: { type: 'object', properties: { chromosome: { type: 'string' }, position: { type: 'number' }, ref: { type: 'string' }, alt: { type: 'string' }, }, required: ['chromosome', 'position', 'ref', 'alt'], }, minItems: 1, }, modality: { type: 'string', enum: ['expression', 'splicing', 'tf_binding', 'chromatin'], description: 'Regulatory modality to screen', }, }, required: ['variants', 'modality'], }, }; export const GENERATE_VARIANT_REPORT_TOOL: Tool = { name: 'generate_variant_report', description: `Generate comprehensive clinical report for a variant. Full analysis with all modalities and clinical interpretation. Perfect for: clinical reports, diagnostic summaries. Example: "Generate full report for chr13:32912345G>T"`, inputSchema: { type: 'object', properties: { chromosome: { type: 'string', pattern: '^chr([1-9]|1[0-9]|2[0-2]|X|Y)$' }, position: { type: 'number', minimum: 1 }, ref: { type: 'string', pattern: '^[ATGCatgc]+$' }, alt: { type: 'string', pattern: '^[ATGCatgc]+$' }, tissue_type: { type: 'string' }, }, required: ['chromosome', 'position', 'ref', 'alt'], }, }; export const EXPLAIN_VARIANT_IMPACT_TOOL: Tool = { name: 'explain_variant_impact', description: `Provide human-readable explanation of variant impact. Translates technical predictions into plain language. Perfect for: patient reports, non-technical summaries. Example: "Explain the impact of chr9:12345678A>C in simple terms"`, inputSchema: { type: 'object', properties: { chromosome: { type: 'string', pattern: '^chr([1-9]|1[0-9]|2[0-2]|X|Y)$' }, position: { type: 'number', minimum: 1 }, ref: { type: 'string', pattern: '^[ATGCatgc]+$' }, alt: { type: 'string', pattern: '^[ATGCatgc]+$' }, tissue_type: { type: 'string' }, }, required: ['chromosome', 'position', 'ref', 'alt'], }, }; /** * All available tools */ export const ALL_TOOLS: Tool[] = [ PREDICT_VARIANT_TOOL, BATCH_SCORE_TOOL, ASSESS_PATHOGENICITY_TOOL, PREDICT_TISSUE_SPECIFIC_TOOL, COMPARE_VARIANTS_TOOL, PREDICT_SPLICE_IMPACT_TOOL, PREDICT_EXPRESSION_IMPACT_TOOL, ANALYZE_GWAS_LOCUS_TOOL, COMPARE_ALLELES_TOOL, BATCH_TISSUE_COMPARISON_TOOL,
• scripts/alphagenome_bridge.py:818-819 (handler)

  Python bridge dispatch: routes 'batch_tissue_comparison' action to the handler function.
  elif action == 'batch_tissue_comparison': result = batch_tissue_comparison(client, params)
• src/alphagenome-client.ts:340-346 (helper)

  TypeScript client bridge method: forwards batch_tissue_comparison params to Python bridge via callPythonBridge.
  async batchTissueComparison(params: any): Promise<any> { try { return await this.callPythonBridge('batch_tissue_comparison', params); } catch (error) { if (error instanceof ApiError) throw error; throw new ApiError(`Batch tissue comparison failed: ${error}`, 500); }