MCP in Production: Deploying Model Context Protocol Servers for Enterprise Tool Integration

The Model Context Protocol (MCP) standardizes how AI agents connect to external data sources and tools. In production, MCP servers need authentication, rate limiting, and audit logging.

1. Production MCP Server Architecture

[ AI Agent (Claude / GPT) ]
           |
           | MCP Protocol (JSON-RPC)
           v
[ MCP Gateway ]
  ├── Authentication (API Key / OAuth)
  ├── Rate Limiting (per-user token budgets)
  ├── Audit Logging (every tool call recorded)
  └── Tool Router
       ├── Database Tools (list_records, query, update)
       ├── File System Tools (read_file, write_file)
       ├── API Tools (http_request, webhook_trigger)
       └── Admin Tools (manage_users, view_logs)

2. Secure MCP Tool Definition

const tools = [
  {
    name: "query_database",
    description: "Execute a read-only SQL query against the production database",
    inputSchema: {
      type: "object",
      properties: {
        query: { type: "string", description: "SELECT query only" },
        limit: { type: "number", maximum: 100 }
      }
    },
    handler: async (input, context) => {
      // Validate: block mutations
      if (/\b(INSERT|UPDATE|DELETE|DROP|ALTER)\b/i.test(input.query)) {
        throw new Error("Only SELECT queries are permitted");
      }
      // Enforce row limits
      const safeQuery = `${input.query} LIMIT ${Math.min(input.limit || 50, 100)}`;
      return await db.query(safeQuery);
    }
  }
];

Production MCP deployments require the same security rigor as any API — authentication, authorization, input validation, and comprehensive audit trails.