"author:munhq" matching MCP servers:

Enables an LLM agent to measure where a session's tokens actually go and whether rewriting history pays for the prompt-cache prefix it invalidates, so it can pick clearing thresholds or decline to compress. Exposes the token-cost accounting and cache-preserving optimization layers over MCP.

Enables AI agents to query multi-cloud costs, inventory, waste, and commitments across AWS, GCP, Cloudflare, and OVH through read-only MCP tools.

Enables AI coding agents to query code structure efficiently through 16 MCP tools, including symbol lookup, full-text search, dependency analysis, and refactor planning, powered by tree-sitter parsing and index-backed code intelligence.

A memory layer for AI coding tools that indexes transcripts from Claude Code, Gemini CLI, Codex, OpenCode, and Antigravity into a unified searchable history, providing 52 MCP tools for agents to recall past sessions, search across tools, and write back decisions.