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

MCP Optimizer is a new tool aimed at hardening coding-focused AI agents by adding “intelligent operational guardrails” while cutting token usage and response latency. The announcement promises a middleware-style layer that intercepts agent actions (for example, a dangerous call like agent.execute({"action":"delete_database","resource":"production_db"})) and enforces policy or validation rules before executing side‑effecting commands. The pitch emphasizes both safety — preventing accidental or malicious destructive operations — and cost-efficiency through token reduction. Technically, MCP’s value proposition centers on runtime action validation and prompt/context optimization: canonicalizing or compressing prompts, applying schema-driven function calls, caching and deduplicating previous responses, and enforcing permission checks at the action level. For practitioners this means lower inference cost, faster turnarounds, and safer production rollouts for agents that interact with real systems (CI/CD, infra, databases). While implementation details aren’t fully disclosed in the brief notice, the approach is significant for ML engineering teams building agents that must balance autonomy, security, and token-budget constraints in real-world deployments.