mongodocs-mcp/dist/cli/status.js

Lightning-fast semantic search for MongoDB documentation via Model Context Protocol. 10,000+ documents, <500ms search.

#!/usr/bin/env node
import { MongoClient } from 'mongodb';
import dotenv from 'dotenv';
import colors from 'colors';
// Load environment variables
dotenv.config();
async function checkStatus() {
    console.log(colors.cyan('\n📊 MONGODB MCP STATUS CHECK'));
    console.log(colors.cyan('='.repeat(50)));
    try {
        // Connect to MongoDB
        console.log(colors.yellow('\n🔌 Connecting to MongoDB...'));
        const client = new MongoClient(process.env.MONGODB_URI);
        await client.connect();
        const cluster = process.env.MONGODB_URI.split('@')[1]?.split('/')[0] || 'Unknown';
        console.log(colors.green(`✅ Connected to: ${cluster}`));
        // Check database and collections
        const dbName = process.env.MONGODB_DATABASE || 'mongodb_semantic_docs';
        const collectionName = process.env.MONGODB_COLLECTION || 'documents';
        console.log(colors.yellow(`\n📚 Checking database: ${dbName}`));
        const db = client.db(dbName);
        const collections = await db.listCollections().toArray();
        if (collections.length === 0) {
            console.log(colors.red('❌ Database is empty!'));
            console.log(colors.yellow('💡 Run: mongodocs-index to populate the database'));
            await client.close();
            return;
        }
        console.log(colors.green(`✅ Found ${collections.length} collections:`));
        let totalDocuments = 0;
        let hasEmbeddings = false;
        for (const collection of collections) {
            const coll = db.collection(collection.name);
            const count = await coll.countDocuments();
            totalDocuments += count;
            const icon = collection.name === collectionName ? '🎯' : '📁';
            const status = collection.name === collectionName ? ' (ACTIVE)' : '';
            console.log(colors.white(`  ${icon} ${collection.name}: ${count} documents${status}`));
            if (count > 0 && collection.name === collectionName) {
                const sample = await coll.findOne();
                if (sample?.embedding) {
                    hasEmbeddings = true;
                    console.log(colors.green(`     ✅ Has embeddings (${sample.embedding.length} dimensions)`));
                }
                else {
                    console.log(colors.red(`     ❌ No embeddings found`));
                }
            }
        }
        // Check vector search index
        console.log(colors.yellow('\n🔍 Checking vector search index...'));
        try {
            const collection = db.collection(collectionName);
            const indexes = await collection.listSearchIndexes().toArray();
            const vectorIndex = indexes.find((i) => i.name === 'semantic_search');
            if (vectorIndex) {
                const indexStatus = vectorIndex.status || 'UNKNOWN';
                const status = indexStatus === 'READY' ? colors.green('READY') : colors.yellow(indexStatus);
                console.log(colors.white(`  📊 Vector index: ${status}`));
            }
            else {
                console.log(colors.red('  ❌ No vector search index found'));
                console.log(colors.yellow('  💡 Create index in MongoDB Atlas for vector search'));
            }
        }
        catch (error) {
            console.log(colors.red('  ❌ Cannot check vector index (may not be supported)'));
        }
        // Environment check
        console.log(colors.yellow('\n⚙️  Environment Configuration:'));
        console.log(colors.white(`  📍 Database: ${dbName}`));
        console.log(colors.white(`  📁 Collection: ${collectionName}`));
        console.log(colors.white(`  🔑 Voyage API: ${process.env.VOYAGE_API_KEY ? '✅ Set' : '❌ Missing'}`));
        // Overall status
        console.log(colors.cyan('\n🎯 OVERALL STATUS:'));
        if (totalDocuments === 0) {
            console.log(colors.red('❌ NOT READY - No documents indexed'));
            console.log(colors.yellow('   Run: mongodocs-index'));
        }
        else if (!hasEmbeddings) {
            console.log(colors.red('❌ NOT READY - Documents exist but no embeddings'));
            console.log(colors.yellow('   Run: mongodocs-index to generate embeddings'));
        }
        else {
            console.log(colors.green('✅ READY FOR MCP!'));
            console.log(colors.white(`   📊 ${totalDocuments} documents with embeddings`));
            console.log(colors.white('   🚀 Run: mongodocs-mcp to start server'));
        }
        await client.close();
    }
    catch (error) {
        console.log(colors.red('\n❌ Status check failed:'));
        console.log(colors.red(`   ${error instanceof Error ? error.message : 'Unknown error'}`));
        if (error instanceof Error && error.message.includes('authentication')) {
            console.log(colors.yellow('\n💡 Check your MONGODB_URI credentials'));
        }
        process.exit(1);
    }
}
// Run if executed directly
if (import.meta.url === `file://${process.argv[1]}`) {
    checkStatus().catch(error => {
        console.error(colors.red('❌ Fatal error:'), error);
        process.exit(1);
    });
}
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