Show HN: MCP Optimizer for faster, lower-token coding agents (platform.tupl.xyz)

MCP Optimizer is a new tool aimed at making coding-focused AI agents both safer and cheaper to run: it promises intelligent, operational guardrails that intercept risky actions (for example, an agent calling agent.execute({"action":"delete_database","resource":"production_db"})) while reducing token consumption to speed up interactions and cut costs. The pitch emphasizes practical deployment needs—policy enforcement, action validation, and token-efficient prompting—so agents can carry out automation without accidentally executing dangerous operations or burning budget on verbose context. For AI/ML teams building autonomous systems, this matters because it addresses two common pain points at once: operational safety and runtime efficiency. Technically, MCP Optimizer appears intended to sit in the agent execution path to validate or sanitize actions, enforce policies, and optimize the prompt/response flow to lower token usage (via techniques like context trimming, response caching, or compact instruction encoding). The result is faster throughput, lower inference cost, and stronger auditability for production agents—useful for CI/CD automation, data pipelines, and developer tooling where both correctness and cost control are critical.