Context bloat occurs when MCP agents accumulate more tool metadata, schemas, and state than models can effectively reason over. This article explains how it happens, why it hurts agent reliability and cost, and how to recognize it in real MCP systems.
MCP defines a standard for connecting LLM's to external tools and services. While traditional observability focuses on infrastructure performance, the success of an MCP server is fundamentally tied to agent-user interaction. This article analyzes the critical, often "hidden," business and behavioral metrics: from client-specific docstring tuning to user journey mapping and sentiment analysis that high-performing MCP server operators use to drive product development, debug complex agent errors, and ensure high Service Level Agreements (SLAs) for enterprise users, as discussed by Shubham Palriwala of Agnost AI.