MCP + llms.txt Integration Flow

1 Developer Request

                    # Developer using AI assistant (Claude, Cursor, etc.)

                    Developer: "Help me integrate with ScyllaDB"

2 AI Discovers llms.txt LIVE

                    # AI automatically fetches

                    GET https://scylladb.com/llms.txt

                    # Response contains MCP configuration

                    mcp:

                      server: wss://mcp.scylladb.com

                      protocol: model-context-protocol

                      tools: [create_keyspace, run_query, benchmark]

3 Auto-Connect to MCP Server

                    # AI establishes WebSocket connection

                    const mcp = await connectMCP('wss://mcp.scylladb.com');

                    # Authenticate with API key

                    await mcp.authenticate({ apiKey: '***' });

                    # Get available tools

                    const tools = await mcp.listTools();

                    // Returns: [create_keyspace, run_query, generate_schema, benchmark, migrate_data]

4 AI Announces Capabilities

                    AI: "I've connected to ScyllaDB's MCP server. I can now:

                      • Create keyspaces and tables

                      • Generate optimized schemas for your use case

                      • Run performance benchmarks

                      • Execute CQL queries

                      • Migrate data from other databases

                    What would you like me to do?"

5 Developer Makes Specific Request

Developer: "Create a schema for a real-time chat application"

6 AI Executes Directly via MCP

                    # AI calls MCP tool directly

                    const schema = await mcp.callTool('generate_schema', {

                      useCase: 'real_time_chat',

                      requirements: ['low_latency', 'message_history', 'user_presence'],

                      scale: '100k_concurrent_users'

                    });

                    # MCP returns optimized schema

                    CREATE KEYSPACE chat_app WITH replication = {

                      'class': 'NetworkTopologyStrategy',

                      'datacenter1': 3

                    };

                    CREATE TABLE messages (

                      room_id UUID,

                      message_id TIMEUUID,

                      user_id UUID,

                      content TEXT,

                      PRIMARY KEY ((room_id), message_id)

                    ) WITH CLUSTERING ORDER BY (message_id DESC);

7 AI Continues Integration

                    AI: "I've created an optimized schema for your chat app. Now I can:

                      • Deploy this schema to your cluster

                      • Generate client code in your preferred language

                      • Set up real-time subscriptions

                      • Run performance benchmarks

                    The schema is optimized for:

                      • Fast message retrieval (most recent first)

                      • Efficient room-based partitioning

                      • Horizontal scaling

                    Shall I deploy it?"

🎯 Result: Zero-Friction Integration

                    # What traditionally takes hours now happens in seconds:

                    ✓ No documentation reading required

                    ✓ No manual API exploration

                    ✓ Best practices built-in

                    ✓ Direct execution capability

                    ✓ Real-time feedback

                    # The AI becomes a live integration assistant

                    # with direct access to execute operations!

> MCP + llms.txt Integration Flow