#406 · Primary category: AI Coding Assistants

token-optimizer-mcp

ai caching claude compression gemini-cli-extension llm mcp mcp-server token-optimization

Measure token savings per AI coding agent, optimize context, and share a live local knowledge graph across 16 CLI clients.

Project last updated:08/30/26

GitHub Stars

501

Forks

59

Contributors

7

License

MIT

Why we included this project

Coding agents often spend their largest context expenses on work they already did: re-reading files that haven't changed, dumping a whole file to see a few lines, rerunning broad searches, and rediscovering conclusions from an earlier session. Token Optimizer is an MCP server that sits between a coding client and those built-in calls, refuses the expensive ones, and hands back a cached, diff-based replacement instead. It works with Codex, Claude Code, Gemini, and other CLI clients (sixteen in total), and it keeps a per-project knowledge graph of findings and decisions, so when an agent touches a related file the answer comes back almost immediately at a fraction of the original token cost. The dashboard also attributes savings to each client and flags the cases where the optimizer itself costs more than it saves. For teams running agents on large codebases, that is a practical way to reclaim context headroom without touching your prompts.

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