Reddit MCP Server

/** * @file MCP Server configuration and capabilities - Example Implementation * @module constants/server/server-config * * @remarks * This module defines the server metadata and capabilities for this example MCP server * implementation. It demonstrates how to properly configure a type-safe MCP server * that implements the full MCP specification including tools, prompts, resources, * sampling, and OAuth 2.1 authentication. * * This serves as a reference implementation for developers building their own MCP servers. * * @see {@link https://modelcontextprotocol.io/specification/2025-06-18/server | MCP Server Specification} */ import type { Implementation, ServerCapabilities } from "@modelcontextprotocol/sdk/types.js"; /** * Server implementation metadata * * @remarks * Provides identifying information about this example MCP server implementation. * This demonstrates proper type-safe configuration with full specification support. * * Key features demonstrated: * - Complete MCP specification implementation * - Type-safe TypeScript architecture * - Production-ready patterns and best practices * - OAuth 2.1 authentication flow * - Real-world API integration (Reddit) */ export const serverConfig: Implementation = { name: "brainloop-mcp-server", version: "2.0.0", metadata: { name: "BRAINLOOP MCP Server", description: "BRAINLOOP MCP server for personalized learning data access. " + "Provides batch operations for courses, units, lessons, and progress tracking. " + "Built with OAuth 2.1 authentication and production-ready architecture.", icon: "brain", color: "blue", serverStartTime: Date.now(), environment: process.env.NODE_ENV || "production", customData: { serverType: "brainloop-production", implementationFeatures: [ "batch-operations", "course-management", "lesson-creation", "progress-tracking", "oauth-2.1", "type-safe", "production-ready" ], integration: "brainloop-api", supportedOperations: [ "create-course-batch", "create-unit-batch", "create-lesson-batch", "get-my-courses", "search-lessons", "get-progress" ], }, }, }; /** * Server capabilities declaration * * @remarks * This declares all MCP capabilities that this example server supports. * Each capability corresponds to a feature in the MCP specification: * * - tools: Interactive functions the AI can call * - sampling: AI content generation with human approval * - prompts: Predefined prompt templates * - resources: Dynamic content the AI can read * - logging: Server-side logging capability * * This example implements ALL capabilities to serve as a complete reference. */ export const serverCapabilities: { capabilities: ServerCapabilities } = { capabilities: { tools: {}, // Full tool support with type-safe handlers sampling: {}, // Complete sampling implementation with callbacks prompts: {}, // Dynamic prompt generation resources: {}, // Resource listing and reading logging: {}, // Client-requested logging support }, }; /** * Additional server configuration constants */ export const SERVER_CONFIG = { SESSION_TIMEOUT: 30 * 60 * 1000, MAX_SESSIONS: 100, RATE_LIMIT: { windowMs: 60000, // 1 minute maxRequests: 100, }, MAX_REQUEST_SIZE: 10 * 1024 * 1024, // 10MB PROTOCOL_VERSION: "2025-06-18", SDK_VERSION: "@modelcontextprotocol/sdk@1.13.0", } as const;