Token Optimizer MCP

What It Is (confidence: medium)

Token Optimizer MCP (ooples/token-optimizer-mcp) is an MCP server + agent plugin that optimizes token usage for AI coding agents (Claude Code, Codex, Cursor, Windsurf, Cline, Gemini CLI, OpenCode, Copilot CLI). Claims “95%+ token reduction” via caching, compression, and smart tool intelligence. Ships a knowledge graph (“the part nothing else has”) built from the user’s own transcripts to measure prompt-cache economics, decide model routing by outcomes, and detect waste.

Distinctive Design (confidence: medium)

• Compaction is consolidation, not loss — frames compaction as lossless consolidation, unlike raw truncation.
• Progressive disclosure that tracks what was already asked, so repeated context is not re-sent.
• Verification against a control arm — “proves its own savings against a control arm,” and an “Honest comparison” section acknowledges what other optimizers do and do not do.
• Enforcing tier — hooks that can refuse a wasteful tool call (pre-execution veto) rather than merely advising.
• Prompt-cache economics measured from your own transcript — cache hits and misses computed from actual sessions, not estimates.

Claims (confidence: low)

The 95%+ headline reduction number is a marketing claim from the repo description; the tool’s own architecture stresses measurement from transcripts and honest comparison rather than a single headline figure. get_optimization_report MCP tool exposes token/compression stats on demand.

Implications (confidence: medium)

Token Optimizer MCP occupies the “measured and enforced” end of the optimizer spectrum: it claims to justify savings against a control arm and refuses wasteful calls. This is the strongest guard against the RTK-style “input savings < output increase” failure, because it measures the whole transcript rather than compressing one command stream. The knowledge-graph/transcript approach is closer to a feedback loop than a static proxy.

Open Questions

• Whether the “95%+” headline survives the tool’s own honest-comparison benchmark at real task mix.
• Whether the pre-execution veto hooks degrade developer trust / agent flow in practice.

Related

rtk-rust-token-killer — proxy-based input compression; the failure mode this tool measures against vibe-coder-mcp — MCP server whose map-codebase tool claims 95-97% token reduction (README, unvalidated); same token-cost lever from inside the agent caveman — complementary output-side reduction: /caveman skill cuts ~65% output tokens, caveman wrap is the local proxy sibling headroom — compression proxy with output shaping token-usage-reduction — the cost-lever space this tool automates skill-reducer — complementary research on compressing authored skill content claude-code-system-prompt — primary target agent (plugin install path)

Sources

• raw/external/github-com-token-optimizer-mcp-987700a8.md