mcp-adr-analysis-server/dist/src/types/conversation-context.js

MCP server for analyzing Architectural Decision Records and project architecture

/**
 * Conversation Context Types
 *
 * Defines the structure for passing conversation context from calling LLMs
 * to the MCP server for context-aware analysis.
 */
/**
 * JSON Schema for conversation context - reusable across tools
 */
export const CONVERSATION_CONTEXT_SCHEMA = {
    type: 'object',
    description: 'Rich context from the calling LLM about user goals and discussion history',
    properties: {
        humanRequest: {
            type: 'string',
            description: 'Original human request text for context restoration and knowledge graph storage'
        },
        userGoals: {
            type: 'array',
            items: { type: 'string' },
            description: 'Primary objectives the user wants to achieve (e.g., ["microservices migration", "improve security"])'
        },
        focusAreas: {
            type: 'array',
            items: { type: 'string' },
            description: 'Specific areas of concern or interest (e.g., ["security", "performance", "maintainability"])'
        },
        constraints: {
            type: 'array',
            items: { type: 'string' },
            description: 'Limitations, compliance requirements, or restrictions (e.g., ["GDPR compliance", "budget under $50k", "minimal downtime"])'
        },
        previousContext: {
            type: 'string',
            description: 'Relevant context from previous conversation (e.g., "User mentioned concerns about database splitting")'
        },
        projectPhase: {
            type: 'string',
            description: 'Current project phase (e.g., "planning", "development", "migration", "production")'
        },
        userRole: {
            type: 'string',
            description: 'User\'s role or expertise level (e.g., "senior architect", "developer", "project manager")'
        },
        requirements: {
            type: 'array',
            items: { type: 'string' },
            description: 'Specific requirements or preferences mentioned'
        },
        timeline: {
            type: 'string',
            description: 'Timeline or urgency information (e.g., "launch in 3 months", "urgent migration")'
        },
        budget: {
            type: 'string',
            description: 'Budget or resource constraints (e.g., "limited budget", "enterprise scale")'
        }
    },
    additionalProperties: false
};
/**
 * Utility function to extract context summary for prompts
 */
export function formatContextForPrompt(context) {
    if (!context)
        return '';
    const sections = [];
    if (context.humanRequest) {
        sections.push(`Human Request: "${context.humanRequest}"`);
    }
    if (context.userGoals?.length) {
        sections.push(`User Goals: ${context.userGoals.join(', ')}`);
    }
    if (context.focusAreas?.length) {
        sections.push(`Focus Areas: ${context.focusAreas.join(', ')}`);
    }
    if (context.constraints?.length) {
        sections.push(`Constraints: ${context.constraints.join(', ')}`);
    }
    if (context.projectPhase) {
        sections.push(`Project Phase: ${context.projectPhase}`);
    }
    if (context.userRole) {
        sections.push(`User Role: ${context.userRole}`);
    }
    if (context.timeline) {
        sections.push(`Timeline: ${context.timeline}`);
    }
    if (context.previousContext) {
        sections.push(`Previous Context: ${context.previousContext}`);
    }
    return sections.length > 0 ? `## User Context\n${sections.join('\n')}\n\n` : '';
}
/**
 * Check if context contains meaningful information
 */
export function hasMeaningfulContext(context) {
    if (!context)
        return false;
    return !!(context.humanRequest ||
        context.userGoals?.length ||
        context.focusAreas?.length ||
        context.constraints?.length ||
        context.previousContext ||
        context.projectPhase ||
        context.userRole ||
        context.requirements?.length);
}
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