MCP doesn't move data. It moves trust

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Abstraction: MCP as AI governance and intent-control layer over APIs

Key points:

MCP uses JSON-RPC 2.0; the model is a client sending structured requests — the MCP server validates, applies policy, executes, and logs; credentials never touch the model
MCP is a control layer, not a transport layer: it defines what AI can do, under what rules, with full auditability; APIs remain the mechanism for actual execution
Key advantages: governance (every tool call logged and revocable), safety (no direct production system access), interoperability (any MCP-speaking model uses approved tools consistently)
MCP enables tool discovery — models can query the server for available capabilities, enabling safer self-aware enterprise tool use
Without APIs, MCP can only recommend; without MCP, APIs are open highways with no traffic rules — autonomy requires both layers
Risks include malicious connectors, misconfigured policies, and audit fatigue; security teams must extend oversight to connectors, not just models

Connections: Model Context Protocol · AI Agents · AI Safety · Anthropic · Openai