nikhilnt
# TokenBurnRate
> See where your AI tokens go — and how to spend less of them.
[](https://npmjs.com/package/token-tracker-mcp)
[](https://npmjs.com/package/token-tracker-mcp)
[](LICENSE)
[](https://nodejs.org)
---
**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.
```
⬡ token-tracker LAST 7 DAYS
────────────────────────────────────────────────────────────────────────
Overview
Total cost $18.7421 (~$80.59/month est.)
API calls 347
Input tokens 4.82M
Cache reads 620K (11.4% hit rate)
Daily Cost
Mon Jun 02 ████████████░░░░░░░░░░░░ $2.14
Tue Jun 03 ████████████████████░░░░ $3.82
Thu Jun 05 ████████████████████████ $4.51
💡 Optimization Hints saves $31.20/mo
● CRIT Prompt cache barely used
Cache hit rate: 11.4% — target is 30–60%
Action: Move static content to top of messages
Est. saving: $12.40/month
● HIGH Expensive model doing test generation
104 test-gen calls on Sonnet costing $2.25/week
Action: Route to claude-haiku-4-5, saves 80%
Est. saving: $7.80/month
```
---
## Install
```bash
npm install -g token-tracker-mcp
```
Or run without installing:
```bash
npx token-tracker-mcp report
```
## Add to Claude Desktop
```bash
node scripts/setup.js
```
Or add manually to `~/Library/Application Support/Claude/claude_desktop_config.json`:
```json
{
"mcpServers": {
"token-tracker": {
"command": "token-tracker",
"args": ["serve"]
}
}
}
```
Restart Claude Desktop. Done.
---
## 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 <id>` | 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 |
---
## 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 |
---
## Optimization Hints Engine
8 deterministic rules — no LLM calls, runs instantly on your local data:
| Hint | Triggers when |
|---|---|
| `cache-utilization` | Cache hit rate < 30% |
| `model-swap-testing` | Test gen running on Sonnet / Opus |
| `model-swap-debug` | Debugging on Opus |
| `verbose-outputs` | Output / input ratio > 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.
---
## Privacy
All data stored at `~/.token-tracker/usage.db` (SQLite).
**Nothing leaves your machine.** No telemetry, no account required.
---
## Development
```bash
git clone https://github.com/nikhilnt1234/TokenBurnRate.git
cd TokenBurnRate
npm install --ignore-scripts
npx tsup
npm test
```
---
## Roadmap
- [ ] Team / Supabase backend (multi-user shared dashboard)
- [ ] Weekly email digest
- [ ] Slack / webhook alerts
- [ ] macOS menubar app
---
## License
MIT © 2026 Nikhil T
TDQS
Scored across 8 tools
All eight tools have clearly distinct purposes: logging, summarizing, hinting, listing models/sessions, setting budgets, and exporting. No two tools overlap in functionality, ensuring unambiguous selection.
Tool names follow a consistent verb_noun pattern with lowercase underscores (e.g., list_models, set_budget, export_csv). No mixing of conventions or verb styles.
With eight tools, the server covers a focused domain (AI usage tracking and optimization) without being too sparse or overwhelming. Each tool serves a distinct and necessary function.
The tool surface covers core workflows: logging, viewing summaries, getting optimization hints, listing models/sessions, setting budgets, and exporting. Minor gap: no tool to delete or reset data, but the domain is primarily read-heavy and logging.