What an MCP Gateway Does That an API Gateway Can't

Enterprises are rapidly deploying Model Context Protocol (MCP) servers that let AI agents access internal systems, creating governance risks when tool access, permissions, audits, and policy enforcement are fragmented. An MCP gateway is presented as a reverse proxy and centralized control plane for agent-to-tool traffic, distinct from MCP servers that execute tool calls and API gateways that manage conventional HTTP endpoint access. Unlike API gateways, MCP gateways can control tool discovery, enforce fine-grained function and parameter policies, propagate the originating user’s identity, support human approval for sensitive actions, route requests across multiple MCP servers, and collect agent-specific telemetry such as token use, costs, tool-call behavior, and audit details. These controls aim to limit unauthorized access, reduce tool overload in model context windows, simplify scaling beyond direct agent-to-server connection meshes, and improve compliance and incident investigation. The piece argues that CData Connect AI can replace separate MCP servers and a gateway for many organizations by offering a managed, unified MCP platform with direct connections to enterprise data sources, authentication, per-user permissions, schema-aware access, and query auditing.

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