ultimate-mcp-server/dist/resources/index.js

The definitive all-in-one Model Context Protocol server for AI-assisted coding across 30+ platforms

export async function registerBuiltInResources(server) {
    // System status resource
    const systemStatus = {
        uri: "ultimate://status",
        name: "System Status",
        description: "Current server status and health information",
        mimeType: "application/json",
        handler: async () => {
            const metrics = server.getMetrics();
            const systemMetrics = metrics.getSystemMetrics();
            return {
                status: "healthy",
                uptime: systemMetrics.uptime,
                memory: {
                    current: systemMetrics.currentMemoryUsage,
                    peak: systemMetrics.peakMemoryUsage,
                },
                requests: {
                    total: systemMetrics.totalRequests,
                    active: systemMetrics.activeRequests,
                },
                timestamp: new Date().toISOString(),
            };
        },
        tags: ["system", "monitoring"],
    };
    // Documentation resource
    const documentation = {
        uri: "ultimate://docs",
        name: "Documentation",
        description: "Server documentation and API reference",
        mimeType: "text/markdown",
        handler: async () => {
            return `# Ultimate MCP Server Documentation

## Overview
The Ultimate MCP Server is a comprehensive, production-ready Model Context Protocol server with advanced AI orchestration, debugging, and problem-solving capabilities.

## Features
- Multi-AI orchestration with 7 different strategies
- Advanced debugging and code analysis tools
- Context persistence with PostgreSQL/Redis
- Comprehensive metrics and monitoring
- Built-in rate limiting and security

## Available Tools
- \`ask\`: Ask a question to a specific AI model
- \`orchestrate\`: Orchestrate tasks across multiple AI models
- \`analyze_error\`: Analyze errors and provide debugging suggestions
- \`explain_code\`: Explain code with detailed breakdown
- \`suggest_optimizations\`: Suggest code optimizations
- \`debugging_session\`: Interactive debugging guidance
- \`generate_code\`: Generate code with best practices
- \`get_metrics\`: Get server performance metrics

## Orchestration Strategies
1. **Sequential**: Chain of thought refinement
2. **Parallel**: Multiple models answer independently
3. **Debate**: Models discuss and refine answers
4. **Consensus**: Models reach agreement
5. **Specialist**: Route to best model for task
6. **Hierarchical**: Tree-based problem decomposition
7. **Mixture**: Mixture of experts approach

## Configuration
Set these environment variables:
- \`OPENROUTER_API_KEY\`: For OpenRouter AI models
- \`ANTHROPIC_API_KEY\`: For Claude models
- \`OPENAI_API_KEY\`: For GPT models
- \`GOOGLE_API_KEY\`: For Gemini models
- \`DATABASE_URL\`: PostgreSQL connection string
- \`REDIS_URL\`: Redis connection string
`;
        },
        tags: ["documentation"],
    };
    // Configuration resource
    const configuration = {
        uri: "ultimate://config",
        name: "Configuration",
        description: "Current server configuration",
        mimeType: "application/json",
        handler: async () => {
            return {
                version: "1.0.0",
                capabilities: {
                    tools: true,
                    resources: true,
                    prompts: true,
                    logging: true,
                },
                providers: {
                    openrouter: !!process.env.OPENROUTER_API_KEY,
                    anthropic: !!process.env.ANTHROPIC_API_KEY,
                    openai: !!process.env.OPENAI_API_KEY,
                    google: !!process.env.GOOGLE_API_KEY,
                },
                persistence: {
                    postgresql: !!process.env.DATABASE_URL,
                    redis: !!process.env.REDIS_URL,
                },
            };
        },
        tags: ["system", "configuration"],
    };
    server.registerResource(systemStatus);
    server.registerResource(documentation);
    server.registerResource(configuration);
}
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