Lossless Compression for the Model Context Protocol: Energy Consumption, Latency, and Bandwidth Trade-Offs

As Large Language Models (LLMs) increasingly operate in agentic environments communicating via the Model Context Protocol (MCP), the structured JSON-RPC message traffic is growing rapidly. Yet the current MCP specification does not include any compression mechanism, leaving potential bandwidth optimizations unaddressed. This paper presents the first empirical energy measurement study of lossless compression applied to MCP payloads on embedded ARM hardware. Four production codecs are evaluated at a single, consistent real-time operating point per codec (LZ4 L0, Zstd L3, gzip L6, Brotli Q4). Across two different datasets, our results show that compression is counterproductive below approximately 50-200 bytes depending on the codec, while savings of up to 50% are achievable for larger payloads. Brotli achieves the highest compression ratio (49.9% combined savings) but incurs the greatest CPU and energy cost. Zstd offers the best practical trade-off, delivering approximately 47% bandwidth reduction with low overhead. Message size is the dominant predictor: below a per-codec break-even of approx. 200 B for LZ4, compression expands rather than shrinks the payload. The apparent per-message-type penalties reflect the prevalence of short messages, not the codec. Message size is identified as the primary predictor of compression benefit, and codec selection guidelines are provided for latency-critical, general-purpose, and bandwidth-constrained MCP deployments on resource-constrained devices.