MCP Sequence Simulation Server

Instructions

Implementation Reference

• src/tools/evolveSequence.ts:64-233 (handler)

  Main handler function that executes the evolve_sequence tool. Implements evolutionary simulation logic including population initialization, fitness calculation, selection pressure application, mutation, and tracking over multiple generations. Supports both DNA and protein sequences with configurable parameters like mutation rate, selection pressure, fitness functions, and output formats.
  async handler({ sequence, generations, populationSize, mutationRate, selectionPressure = 0, fitnessFunction = "gc-content", targetValue = 0.5, seed, trackLineages = false, outputFormat = "summary" }: { sequence: string; generations: number; populationSize: number; mutationRate: number; selectionPressure?: number; fitnessFunction?: string; targetValue?: number; seed?: number; trackLineages?: boolean; outputFormat?: string; }) { const generator = new SequenceGenerator(seed); const evolutionParams: EvolutionParameters = { generations, populationSize, mutationRate, selectionPressure }; const isDNA = /^[ATGC]+$/i.test(sequence.replace(/\s/g, '')); let population = Array(populationSize).fill(null).map(() => ({ sequence: sequence.toUpperCase(), fitness: 0, generation: 0, lineageId: Math.random().toString(36).substr(2, 9) })); const evolutionHistory = []; const lineageData: Array<{ generation: number; lineageId: string; sequence: string; fitness: number; }> | undefined = trackLineages ? [] : undefined; for (let gen = 0; gen <= generations; gen++) { population.forEach(individual => { individual.fitness = calculateFitness(individual.sequence, fitnessFunction, targetValue, isDNA); }); population.sort((a, b) => b.fitness - a.fitness); const genStats = { generation: gen, populationSize: population.length, bestFitness: population[0].fitness, worstFitness: population[population.length - 1].fitness, averageFitness: population.reduce((sum, ind) => sum + ind.fitness, 0) / population.length, bestSequence: population[0].sequence, diversity: calculateDiversity(population.map(ind => ind.sequence)) }; evolutionHistory.push(genStats); if (trackLineages && lineageData) { population.forEach(individual => { lineageData.push({ generation: gen, lineageId: individual.lineageId, sequence: individual.sequence, fitness: individual.fitness }); }); } if (gen < generations) { const newPopulation = []; if (selectionPressure > 0) { const selectionThreshold = Math.floor(populationSize * (1 - selectionPressure)); const survivors = population.slice(0, Math.max(2, selectionThreshold)); while (newPopulation.length < populationSize) { const parent = survivors[Math.floor(Math.random() * survivors.length)]; let newSequence = parent.sequence; if (isDNA) { newSequence = generator.mutateDNA(newSequence, { substitutionRate: mutationRate }); } else { newSequence = mutateProtein(newSequence, mutationRate, generator); } newPopulation.push({ sequence: newSequence, fitness: 0, generation: gen + 1, lineageId: parent.lineageId + '_' + newPopulation.length }); } } else { population.forEach(individual => { let newSequence = individual.sequence; if (isDNA) { newSequence = generator.mutateDNA(newSequence, { substitutionRate: mutationRate }); } else { newSequence = mutateProtein(newSequence, mutationRate, generator); } newPopulation.push({ sequence: newSequence, fitness: 0, generation: gen + 1, lineageId: individual.lineageId }); }); } population = newPopulation; } } let output = ''; if (outputFormat === 'fasta') { const finalPop = population.slice(0, Math.min(10, population.length)); output = finalPop.map((ind, i) => `>evolved_seq_${i + 1}_gen_${generations} fitness=${ind.fitness.toFixed(4)}\n${ind.sequence}` ).join('\n\n'); } const summary = { initialSequence: sequence, finalBestSequence: population[0].sequence, totalGenerations: generations, populationSize, mutationRate, selectionPressure, fitnessFunction, targetValue, finalBestFitness: population[0].fitness, improvementRatio: population[0].fitness / evolutionHistory[0].bestFitness, seed: seed || "random" }; const result: any = { summary, evolutionHistory: outputFormat === 'detailed' ? evolutionHistory : evolutionHistory.filter((_, i) => i % Math.max(1, Math.floor(generations / 20)) === 0 || i === generations) }; if (outputFormat === 'fasta') { result.fastaOutput = output; } if (trackLineages && lineageData) { result.lineageData = lineageData; } if (outputFormat === 'detailed') { result.finalPopulation = population.slice(0, 10); } return { content: [{ type: "text", text: JSON.stringify(result, null, 2) }] }; }
• src/tools/evolveSequence.ts:5-63 (schema)

  Tool definition and input schema for evolve_sequence. Defines parameters: sequence (required), generations (required), populationSize (required), mutationRate (required), selectionPressure, fitnessFunction (enum: gc-content, length, hydrophobic, custom), targetValue, seed, trackLineages, and outputFormat (enum: summary, detailed, fasta).
  definition: { name: "evolve_sequence", description: "Simulate evolution of sequences over multiple generations", inputSchema: { type: "object", properties: { sequence: { type: "string", description: "Starting sequence" }, generations: { type: "number", description: "Number of generations to simulate", minimum: 1 }, populationSize: { type: "number", description: "Population size for each generation", minimum: 2 }, mutationRate: { type: "number", description: "Mutation rate per generation (0-1)", minimum: 0, maximum: 1 }, selectionPressure: { type: "number", description: "Selection pressure (0-1), 0 = no selection, 1 = strong selection", minimum: 0, maximum: 1 }, fitnessFunction: { type: "string", description: "Fitness function: 'gc-content', 'length', 'hydrophobic', or 'custom'", enum: ["gc-content", "length", "hydrophobic", "custom"] }, targetValue: { type: "number", description: "Target value for fitness function (e.g., target GC content)" }, seed: { type: "number", description: "Random seed for reproducible results (optional)" }, trackLineages: { type: "boolean", description: "Track individual lineages through generations", default: false }, outputFormat: { type: "string", description: "Output format: 'summary', 'detailed', or 'fasta'", enum: ["summary", "detailed", "fasta"] } }, required: ["sequence", "generations", "populationSize", "mutationRate"] }, },
• src/server.ts:26-37 (registration)

  Registration of evolve_sequence tool in ListToolsRequestSchema handler. The tool definition is exported as part of the tools list available to MCP clients.
  server.setRequestHandler(ListToolsRequestSchema, async () => { return { tools: [ generateDNA.definition, generateProtein.definition, mutateSequence.definition, evolveSequence.definition, simulatePhylogeny.definition, simulateFastq.definition, ], }; });
• src/server.ts:80-92 (registration)

  Routing of evolve_sequence tool calls in CallToolRequestSchema handler. Maps the tool name 'evolve_sequence' to its handler function with proper type casting of arguments.
  case "evolve_sequence": return await evolveSequence.handler(args as { sequence: string; generations: number; populationSize: number; mutationRate: number; selectionPressure?: number; fitnessFunction?: string; targetValue?: number; seed?: number; trackLineages?: boolean; outputFormat?: string; });
• src/tools/evolveSequence.ts:236-278 (helper)

  Helper functions supporting the evolve_sequence handler: calculateFitness (evaluates sequence fitness based on gc-content, length, or hydrophobicity), calculateDiversity (measures population diversity), and mutateProtein (applies mutations to protein sequences).
  function calculateFitness(sequence: string, fitnessFunction: string, targetValue: number, isDNA: boolean): number { switch (fitnessFunction) { case 'gc-content': if (!isDNA) return 0; const gcContent = (sequence.match(/[GC]/gi) || []).length / sequence.length; return 1 - Math.abs(gcContent - targetValue); case 'length': return 1 - Math.abs(sequence.length - targetValue) / Math.max(sequence.length, targetValue); case 'hydrophobic': if (isDNA) return 0; const hydrophobic = new Set(['A', 'V', 'I', 'L', 'M', 'F', 'Y', 'W']); const hydrophobicRatio = sequence.split('').filter(aa => hydrophobic.has(aa)).length / sequence.length; return 1 - Math.abs(hydrophobicRatio - targetValue); default: return Math.random(); } } function calculateDiversity(sequences: string[]): number { const uniqueSequences = new Set(sequences); return uniqueSequences.size / sequences.length; } function mutateProtein(sequence: string, mutationRate: number, generator: SequenceGenerator): string { const aminoAcids = ['A', 'R', 'N', 'D', 'C', 'Q', 'E', 'G', 'H', 'I', 'L', 'K', 'M', 'F', 'P', 'S', 'T', 'W', 'Y', 'V']; let mutated = sequence.split(''); for (let i = 0; i < mutated.length; i++) { if (Math.random() < mutationRate) { const currentAA = mutated[i]; let newAA; do { newAA = aminoAcids[Math.floor(Math.random() * aminoAcids.length)]; } while (newAA === currentAA); mutated[i] = newAA; } } return mutated.join(''); }