Tokenscope

MCP सर्वर जो AI एजेंटों को Claude Code सत्र लागत और संदर्भ आरोपण का विश्लेषण करने देता है — टोकन उपयोग, कैश्ड पुनः-पठन, और कैश लेखन। स्थानीय, केवल-पठन।

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tokenscope ⏣

See what your AI-coding session actually cost — and what's eating your context. A local, read-only CLI that parses your Claude Code session logs and shows where the money goes: model output vs. context being re-sent every turn (the hidden 60%+ of most bills).

$ npx @wartzar-bee/tokenscope

  tokenscope ⏣  latest session
  ──────────────────────────────────────────────────────
  Total cost   $868.84   over 967 model turns

  Where the money went
  output (model writing)     ████░░░░░░░░░░░░░░░░░░░░  16%  $137.24
  cache read (re-sent ctx)   ████████████████░░░░░░░░  66%  $577.59
  cache write (new ctx)      ████░░░░░░░░░░░░░░░░░░░░  18%  $153.67

  Context size per turn  (peak 822k · avg 404k · now 822k tokens)
  ▁▁▁▁▁▂▂▂▂▂▂▂▃▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▆▆▆▆▇▇▇▇▇▇█

  Insights
  • Re-sent (cached) context cost $577.59 (66% of spend) — context re-read every turn.
  • Peak context ~822k tokens — /compact or a fresh session would cut per-turn cost.
  • Only 16% of spend is the model's actual output.

(A real session, default Opus pricing. Your numbers will differ — prices are overridable.)

Why

Agentic coding (Claude Code, etc.) produces surprise bills, and the cause is mundane: as a session grows, the whole context is re-sent every turn, so cost balloons even when the model writes little. Existing dashboards show totals; tokenscope shows the attribution — output vs. cache-read vs. cache-write vs. fresh input, the per-turn context-growth curve, cost by model, subagent spend, and which tools fill your context — with concrete "trim this" insights.

Try it in 10 seconds (no Claude Code logs needed)

npx @wartzar-bee/tokenscope --demo

Runs on a bundled sample session so you see the full report before pointing it at your own logs — no setup, nothing to configure. (The sample is synthetic, for demonstration.)

Install / run

No install — runs via npx:

npx @wartzar-bee/tokenscope               # your most recent Claude Code session
npx @wartzar-bee/tokenscope --demo        # a bundled sample session — no logs needed
npx @wartzar-bee/tokenscope --all         # aggregate every session
npx @wartzar-bee/tokenscope <file|dir>    # a specific session .jsonl
npx @wartzar-bee/tokenscope --version     # print the installed version and exit
npx @wartzar-bee/tokenscope --json        # machine-readable
npx @wartzar-bee/tokenscope --share       # privacy-safe shareable summary (markdown + SVG card)
npx @wartzar-bee/tokenscope --share-svg   # just the SVG "cost report card"
npx @wartzar-bee/tokenscope scan          # static token footprint of a source dir (the engine behind ci-guardrail)
npx @wartzar-bee/tokenscope scan --diff ../base   # cost delta of the current dir vs a base dir — catch a regression before you push
npx @wartzar-bee/tokenscope scan --max-total 50000            # exit 1 if the footprint exceeds a budget — a local cost gate
npx @wartzar-bee/tokenscope scan --diff ../base --max-delta 2000   # exit 1 if the diff adds more than N tokens

Reads ~/.claude/projects/**/*.jsonl. Read-only, local, no network, no telemetry — open the source; nothing leaves your machine.

Local cost gate (pre-push / pre-commit)

--max-total N / --max-delta N make scan exit 1 when the token footprint (or a diff's delta) blows a budget — the same check ci-guardrail runs in CI, but locally, before you push. Wire the absolute budget into a git hook so a runaway prompt/config never leaves your machine:

# .git/hooks/pre-push  (chmod +x)
npx -y @wartzar-bee/tokenscope scan --dir prompts --max-total 50000 \
  || { echo "prompt token footprint over budget — trim before pushing"; exit 1; }

Under budget it prints the report and exits 0; over budget it prints a BLOCKED: line and exits 1. Without a --max-* flag scan just reports (exit 0), so it's opt-in. --max-delta gates the delta between two directories on disk (scan --diff <baseDir> --max-delta N) — point it at a checked-out base tree when you want a regression gate rather than an absolute cap.

Using the pre-commit framework? Add tokenscope to your .pre-commit-config.yaml — no git-hook scripting:

repos:
  - repo: https://github.com/wartzar-bee/tokenscope
    rev: v0.2.6
    hooks:
      - id: tokenscope
        args: ["--dir", "prompts", "--max-total", "50000"]   # optional — omit to just report

language: node, zero dependencies. With no args it prints the footprint (exit 0); add --max-total N (or --diff <baseDir> --max-delta N) to fail the commit over budget.

Share your bill (privacy-safe)

--share emits a compact summary built from aggregate numbers only — no file paths, no prompt/response content — so it's safe to paste in public:

Markdown for Reddit / Discord / a GitHub issue (total, the output/cache-read/cache-write/fresh split with %, peak/avg context, and the headline "X% of spend was re-sent context").
A self-contained SVG "cost report card" (--share-svg) — no binary deps; renders inline on GitHub and is trivially shareable.
How you compare — both forms now answer "is my session unusual?" against a shipped, offline reference set of real sessions (e.g. "more cache-efficient than ~80% of measured sessions; median session re-sends 24%"). It's a reference yardstick, not a census — full honest distribution at tokenscope.pages.dev/benchmark.

Prefer not to touch a terminal flag? The same render runs entirely in your browser at the web surface in web/: paste your --json output and it draws the full report + the SVG card locally — nothing is uploaded.

Use it from an AI agent (MCP server)

There's an MCP server that exposes the same engine to AI agents / MCP clients (Claude Desktop, Claude Code, etc.) as tools: analyze_claude_cost, get_cost_benchmark, and tokenscope_share_summary. Add it to your MCP config:

{ "mcpServers": { "tokenscope": { "command": "npx", "args": ["-y", "@wartzar-bee/tokenscope-mcp"] } } }

Then ask your agent "use tokenscope to analyze my last Claude Code session." It's the same local, read-only engine — see mcp/README.md.

Pricing

Uses documented default prices (Anthropic cache multipliers: write 1.25×/2×, read 0.1× of input). Verify and override for your exact model/tier via ./.tokenscope.json:

{ "pricing": { "claude-opus-4": { "in": 15, "out": 75 } } }

Unknown models are flagged (never silently counted as $0). Token counts are read straight from the logs; cost = those counts × the prices shown.

Part of the wartzar-bee cost toolkit

tokenscope is the measurement engine behind a sibling tool, and one of three open-source cost projects:

ci-guardrail — a GitHub Action that runs tokenscope in CI to predict a pull request's token-cost delta and comment on the responsible files (report-only, or fail the build past a threshold): uses: wartzar-bee/ci-guardrail@v1.
enclave — the security-first, self-hosted runtime & sandbox the wartzar-bee agent fleet runs on (Apache-2.0).

If you find tokenscope useful, ci-guardrail is the zero-config way to run it on every PR.

Why this exists — further reading

tokenscope came out of running autonomous agents and watching the bill. The write-ups behind it:

I put an agent on a timer — overnight it burned 136M tokens doing almost nothing — the runaway-cost postmortem that started this.
Where your Claude Code bill actually goes — I measured 66 of my own sessions — the empirical breakdown tokenscope automates.
The Claude Code cost formula: why the same session can cost 10× more tomorrow — the cost mechanics tokenscope surfaces.
Cost-audit series — reproducible token-cost audits of popular agent frameworks (LangChain, AutoGen, CrewAI, …).
Catch token-cost regressions in CI before they ship — tokenscope as a GitHub Action cost gate on your PRs.

Status / roadmap

v0.1: Claude Code session cost + context attribution + insights. 20/20 unit tests on the cost math (npm test).
Next (evidence-driven): per-tool/-file token attribution; daily/budget alerts; a --watch live meter; OpenAI/Codex log support.

MIT. Not affiliated with Anthropic.