When architecting AI agents that execute multi-step planning loops, tool invocation latency is frequently dismissed as a rounding error compared to model token generation. However, in autonomous engineering agents (like Cursor Agent or Claude Desktop executing 10 to 15 sequential queries to triage a codebase or inspect an infrastructure cluster), transport and serialization overhead compound rapidly. We benchmarked 10,000 tool executions across the two primary Model Context Protocol (MCP) transport models: Stdio and Server-Sent Events (SSE). 1. The Numbers: Invocation Latency Metric Stdio (UNIX Pipe / IPC) Remote SSE (HTTP/1.1 + TLS) Mean Latency 2.1 ms 19.4 ms p95 Latency 3.8 ms 32.1 ms p99 Latency 6.2 ms 48.7 ms Connection Setup 0 ms (Persistent Pipe) 45 ms (TCP Handshake + TLS) 2. Memory Footprint Node.js Stdio Worker: ~32MB RSS per active process. Python FastMCP Worker: ~21MB RSS per process. Compiled Go/Rust Worker: <7MB RSS per process. Centralized SSE Daemon: ~42MB shared across all incoming client streams. 3. Architecture Verdict For developer workstations and desktop agents (Claude Desktop, Cursor), stdio is strictly superior: sub-3ms invocation, zero network port binding, and OS-supervised sandboxing. For multi-tenant cloud environments where agents share access to a centralized cluster or database, SSE behind an Envoy or Traefik reverse proxy provides the necessary mTLS authentication and rate-limiting controls. Explore our full benchmark suite, architecture comparisons, and verified native server recipes at MCP Bridge.