bc-code-intelligence-mcp/dist/types/bc-knowledge.js

BC Code Intelligence MCP Server - Complete Specialist Bundle with AI-driven expert consultation, seamless handoffs, and context-preserving workflows

import { z } from 'zod';
/**
 * Business Central Knowledge Base Types
 *
 * These types define the structure of our atomic BC knowledge topics,
 * indexes, and relationships for intelligent AI consumption.
 */
// YAML Frontmatter Schema - Extended for structured knowledge types
export const AtomicTopicFrontmatterSchema = z.object({
    title: z.string().optional().describe("Human-readable topic title"),
    domain: z.union([
        z.string(),
        z.array(z.string())
    ]).optional().describe("Knowledge domain(s) - single domain or array for shared topics"),
    difficulty: z.enum(["beginner", "intermediate", "advanced", "expert"]).optional().describe("Complexity level"),
    bc_versions: z.string().optional().describe("Supported BC versions (e.g., '14+', '18+')"),
    tags: z.array(z.string()).optional().describe("Searchable tags for topic discovery"),
    prerequisites: z.array(z.string()).optional().describe("Required prerequisite topics"),
    related_topics: z.array(z.string()).optional().describe("Related and complementary topics"),
    samples: z.string().optional().describe("Path to companion sample file"),
    estimated_time: z.string().optional().describe("Time to read/implement (e.g., '15 minutes')"),
    // Extended properties for structured knowledge types
    type: z.string().optional().describe("Knowledge type (code-pattern, workflow, etc.)"),
    name: z.string().optional().describe("Unique name for structured types"),
    pattern_type: z.enum(["good", "bad", "unknown"]).optional().describe("Pattern classification"),
    regex_patterns: z.array(z.string()).optional().describe("Regex patterns for code detection"),
    description: z.string().optional().describe("Brief description for structured types"),
    category: z.string().optional().describe("Category classification"),
    severity: z.enum(["low", "medium", "high", "critical"]).optional().describe("Severity level"),
    impact_level: z.enum(["low", "medium", "high"]).optional().describe("Impact level"),
    detection_confidence: z.enum(["low", "medium", "high"]).optional().describe("Detection confidence"),
    // Workflow-specific properties
    workflow_type: z.string().optional().describe("Type of workflow (checklist, procedure, etc.)"),
    phases: z.array(z.any()).optional().describe("Workflow phases"),
    // Conditional MCP integration
    conditional_mcp: z.string().optional().describe("MCP server ID required for this topic (show only if MCP present)"),
    conditional_mcp_missing: z.string().optional().describe("MCP server ID that excludes this topic (show only if MCP absent)"),
});
// Domain Catalog Structure
export const DomainInfoSchema = z.object({
    title: z.string(),
    description: z.string(),
    topic_count: z.number(),
    subdirectories: z.array(z.string()),
    key_topics: z.array(z.string()),
    difficulty_levels: z.record(z.number()),
    tags: z.array(z.string()),
    sample_files: z.number().optional(),
});
// Utility functions for domain handling
export function isDomainMatch(topicDomain, targetDomain) {
    if (!topicDomain)
        return false;
    if (typeof topicDomain === 'string') {
        return topicDomain === targetDomain;
    }
    if (Array.isArray(topicDomain)) {
        return topicDomain.includes(targetDomain);
    }
    return false;
}
export function getDomainList(domain) {
    if (!domain)
        return [];
    if (typeof domain === 'string')
        return [domain];
    return domain;
}
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