mcp-adr-analysis-server

/** * CE-MCP Prompt Catalog * * Provides lazy loading of prompts - instead of importing all 6,145 lines * of prompt code upfront, this catalog allows on-demand loading of specific * prompt sections, reducing token usage by ~96%. * * @see ADR-014: CE-MCP Architecture */ import { PromptCatalog, PromptCatalogEntry } from '../types/ce-mcp.js'; /** * Prompt catalog with metadata for lazy loading * * Token estimates based on ~4 chars per token average */ export const PROMPT_CATALOG: PromptCatalog = { // ADR-related prompts 'adr-suggestion': { file: 'adr-suggestion-prompts.ts', tokens: 1830, category: 'adr', sections: [ 'implicit_decisions', 'tech_debt', 'security_decisions', 'cross_cutting', 'recommendation_template', ], loadOnDemand: true, }, // Deployment analysis prompts 'deployment-analysis': { file: 'deployment-analysis-prompts.ts', tokens: 3150, category: 'deployment', sections: [ 'readiness_check', 'validation_criteria', 'rollback_plan', 'infrastructure_review', 'security_scan', ], loadOnDemand: true, }, // Environment analysis prompts 'environment-analysis': { file: 'environment-analysis-prompts.ts', tokens: 3050, category: 'analysis', sections: ['dependency_scan', 'config_validation', 'compliance_check', 'resource_assessment'], loadOnDemand: true, }, // Research question prompts 'research-question': { file: 'research-question-prompts.ts', tokens: 3120, category: 'research', sections: ['question_generation', 'research_plan', 'source_evaluation', 'synthesis_template'], loadOnDemand: true, }, // Rule generation prompts 'rule-generation': { file: 'rule-generation-prompts.ts', tokens: 2850, category: 'rules', sections: ['rule_template', 'validation_rules', 'enforcement_policy', 'exception_handling'], loadOnDemand: true, }, // General analysis prompts analysis: { file: 'analysis-prompts.ts', tokens: 2310, category: 'analysis', sections: ['project_analysis', 'code_review', 'architecture_assessment', 'quality_metrics'], loadOnDemand: true, }, // Research integration prompts 'research-integration': { file: 'research-integration-prompts.ts', tokens: 1785, category: 'research', sections: ['integration_strategy', 'synthesis_plan', 'recommendation_format'], loadOnDemand: true, }, // Validated pattern prompts 'validated-pattern': { file: 'validated-pattern-prompts.ts', tokens: 1565, category: 'deployment', sections: ['pattern_detection', 'validation_criteria', 'deployment_guidance'], loadOnDemand: true, }, // Security prompts security: { file: 'security-prompts.ts', tokens: 1270, category: 'security', sections: ['vulnerability_scan', 'masking_rules', 'compliance_check'], loadOnDemand: true, }, // Main index (orchestration) index: { file: 'index.ts', tokens: 3700, category: 'analysis', sections: ['orchestration', 'execution_flow', 'error_handling'], dependencies: ['adr-suggestion', 'deployment-analysis', 'analysis'], loadOnDemand: false, // Core orchestration loaded at startup }, }; /** * Get total token count if all prompts were loaded */ export function getTotalPromptTokens(): number { return Object.values(PROMPT_CATALOG).reduce((sum, entry) => sum + entry.tokens, 0); } /** * Get prompts by category */ export function getPromptsByCategory(category: PromptCatalogEntry['category']): string[] { return Object.entries(PROMPT_CATALOG) .filter(([_, entry]) => entry.category === category) .map(([name]) => name); } /** * Get prompt metadata */ export function getPromptMetadata(promptName: string): PromptCatalogEntry | undefined { return PROMPT_CATALOG[promptName]; } /** * Prompt Loader Service * * Handles lazy loading of prompts with caching */ export class PromptLoader { private cache: Map<string, { content: string; sections: Map<string, string>; expiry: number }> = new Map(); private cacheTTL: number; constructor(cacheTTL: number = 3600) { this.cacheTTL = cacheTTL; } /** * Load a specific prompt section * * @param promptName - Name of the prompt (e.g., 'adr-suggestion') * @param section - Optional section within the prompt * @returns Promise resolving to the prompt content */ async loadPrompt(promptName: string, section?: string): Promise<string> { const metadata = PROMPT_CATALOG[promptName]; if (!metadata) { throw new Error(`Unknown prompt: ${promptName}`); } // Check cache const cacheKey = promptName; const cached = this.cache.get(cacheKey); if (cached && Date.now() < cached.expiry) { if (section) { const sectionContent = cached.sections.get(section); if (sectionContent) { return sectionContent; } } return cached.content; } // Load prompt dynamically const content = await this.loadPromptFile(metadata.file); // Parse sections if needed const sections = this.parseSections(content, metadata.sections); // Cache the result this.cache.set(cacheKey, { content, sections, expiry: Date.now() + this.cacheTTL * 1000, }); if (section) { const sectionContent = sections.get(section); if (sectionContent) { return sectionContent; } throw new Error(`Section '${section}' not found in prompt '${promptName}'`); } return content; } /** * Load prompt file content */ private async loadPromptFile(fileName: string): Promise<string> { // In production, this would use dynamic import or file system read // For now, return a placeholder that indicates which file should be loaded return `[PROMPT_FILE: ${fileName}]`; } /** * Parse sections from prompt content */ private parseSections(content: string, sectionNames: string[]): Map<string, string> { const sections = new Map<string, string>(); // Basic section parsing - looks for patterns like: // // SECTION: section_name // or // export const SECTION_NAME = ... for (const sectionName of sectionNames) { // Try to find section marker const markerRegex = new RegExp( `(?:\\/\\/ SECTION: ${sectionName}|export const ${sectionName.toUpperCase().replace(/-/g, '_')})`, 'i' ); const match = content.match(markerRegex); if (match && match.index !== undefined) { // Extract content until next section or end const startIndex = match.index; const nextSectionMatch = content .slice(startIndex + 1) .match(/(?:\/\/ SECTION:|export const [A-Z_]+)/); const endIndex = nextSectionMatch ? startIndex + 1 + (nextSectionMatch.index || content.length) : content.length; sections.set(sectionName, content.slice(startIndex, endIndex).trim()); } } return sections; } /** * Preload prompts that are commonly used together */ async preloadPromptGroup(promptNames: string[]): Promise<void> { await Promise.all(promptNames.map(name => this.loadPrompt(name))); } /** * Get estimated tokens for a prompt */ getEstimatedTokens(promptName: string, section?: string): number { const metadata = PROMPT_CATALOG[promptName]; if (!metadata) return 0; if (section) { // Estimate section as portion of total const sectionCount = metadata.sections.length; return Math.ceil(metadata.tokens / sectionCount); } return metadata.tokens; } /** * Get recommendations for which prompts to load */ getLoadRecommendations(taskType: string): string[] { const recommendations: Record<string, string[]> = { adr_analysis: ['adr-suggestion', 'analysis'], deployment: ['deployment-analysis', 'validated-pattern', 'environment-analysis'], security: ['security', 'analysis'], research: ['research-question', 'research-integration'], rules: ['rule-generation', 'analysis'], }; return recommendations[taskType] || ['analysis']; } /** * Clear cache */ clearCache(): void { this.cache.clear(); } /** * Get cache statistics */ getCacheStats(): { size: number; entries: string[] } { return { size: this.cache.size, entries: Array.from(this.cache.keys()), }; } } /** * Global prompt loader instance */ let globalPromptLoader: PromptLoader | null = null; /** * Get or create the global prompt loader */ export function getPromptLoader(cacheTTL?: number): PromptLoader { if (!globalPromptLoader) { globalPromptLoader = new PromptLoader(cacheTTL); } return globalPromptLoader; } /** * Reset the global prompt loader */ export function resetPromptLoader(): void { globalPromptLoader = null; } /** * Token savings calculation */ export function calculateTokenSavings(loadedPrompts: string[]): { loaded: number; total: number; saved: number; percentage: number; } { const total = getTotalPromptTokens(); const loaded = loadedPrompts.reduce((sum, name) => { const metadata = PROMPT_CATALOG[name]; return sum + (metadata?.tokens || 0); }, 0); const saved = total - loaded; const percentage = Math.round((saved / total) * 100); return { loaded, total, saved, percentage }; }