TokenBurnRate
以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →
> See where your AI tokens go — and how to spend less of them.
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**TokenBurnRate** is an MCP server + CLI that logs every Claude / GPT / Gemini API call locally, shows you a cost dashboard in your terminal, and tells you *exactly* how to reduce that cost.
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Install
Or run without installing:
Add to Claude Desktop
Or add manually to ~/Library/Application Support/Claude/claude_desktop_config.json:
Restart Claude Desktop. Done.
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CLI Commands
| Command | Description |
|---|---|
| token-tracker report | Full 7-day dashboard |
| token-tracker report --period month | Monthly report |
| token-tracker today | Today only |
| token-tracker hints | Optimization hints ranked by $ saving |
| token-tracker hint | Deep-dive on one hint |
| token-tracker status | One-line: cost · cache % · top hint |
| token-tracker budget | Budget gauges |
| token-tracker models | Pricing table for all models |
| token-tracker export > out.csv | Raw CSV export |
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MCP Tools (use inside Claude)
| Tool | Description |
|---|---|
| log_usage | Log an API call — auto-calculates cost |
| get_summary | Summary for today / week / month / all |
| get_hints | Ranked optimization hints with $ savings |
| get_hint_detail | Deep-dive on a specific hint |
| set_budget | Set a daily / weekly / monthly spend limit |
| list_sessions | Sessions ranked by cost |
| list_models | Pricing table |
| export_csv | CSV dump |
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Optimization Hints Engine
8 deterministic rules — no LLM calls, runs instantly on your local data:
| Hint | Triggers when |
|---|---|
| cache-utilization | Cache hit rate 0.35 |
| session-spike | Any session costs 3× your average |
| context-bloat | Avg tokens / call > 8K |
| retry-loops | Sessions with 30+ high-token calls |
| single-model-dependency | 100% traffic on one expensive model |
Each hint includes severity · evidence · recommended action · estimated monthly saving.
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Privacy
All data stored at ~/.token-tracker/usage.db (SQLite).
**Nothing leaves your machine.** No telemetry, no account required.