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Structural code intelligence MCP server for AI-assisted development. Navigate and edit code by symbol names instead of raw text — symbol lookup, dependency grap
Structural code intelligence MCP server for AI-assisted development. Navigate and edit code by symbol names instead of raw text — symbol lookup, dependency graphs, call chains, dead code detection, and structural diff. Replaces grep/cat with semantic tools: 87% token reduction on large codebases.
One MCP server. One profile. 97.9% on tsbench at -80% tokens. Structural code navigation, persistent memory, and Bash output compaction for AI coding agents.
Version PyPI Tests Benchmark Python 3.11+ MCP CI
mibayy.github.io/token-savior -- project site + benchmark landing github.com/Mibayy/tsbench -- benchmark source + fixtures
| Plain Claude Code | With Token Savior | |
|---|---|---|
| Score | 141 / 180 (78.3%) | 188 / 192 (97.9%) |
| Active tokens / task | 17 221 | 3 395 (-80%) |
| Wall time / task | 110.6 s | 18.9 s (-83%) |
Reproduces with the optimized profile (single env var). See BENCHMARK-SUMMARY.
Real-world bench against 7 days of transcripts (1130 Bash outputs) drove this release. Cumulative savings now sit at ~20.4 K tokens/week (19.3% match rate, 68.9% mean compaction) vs ~12 K/week on v4.2.0.
pytest regex: now matches python3 -m pytest, uv run pytest,
venv-prefixed forms, poetry/hatch/pdm/rye run pytest.fetch, checkout, branch, worktree list,
stash list.gh repo view, gh pr view, gh issue view,
gh pr diff.grep, find, cat compactors. Group hits by file, strip common
prefix, head/tail truncation. 83-96% savings on the fixtures.cd /root/foo && git status now compacts
by picking the last meaningful segment of &&/; chains. Bails on
subshells, heredocs, pipes, loops, unterminated quotes.jest, vitest, eslint, biome, kubectl get/logs, aws sts/ec2/lambda/logs/iam/dynamodb/s3, npm/yarn/pnpm list, pip list/show, curl. Peaks: 91.7% on aws ec2, 95% on
jest all-green.capture_get
if it needs the detail.ts init --agent {claude,cursor,gemini,codex} CLI. Detects agent
settings, deep-merges the hook config, dedups by (matcher, command),
prints a unified diff, backs up settings.json, idempotent on re-run.ts_discover cross-project + format="adoption" reports TS-vs-native
ratios per session with first/second-half trend.git status/diff/log/ push/commit/add, pytest, cargo test/build/clippy, tsc, docker ps/logs, gh run list/view. Median 63%, peak 100% (a green pytest -q collapses to one line).git status -> --porcelain=v2 --branch, tsc ->
--pretty false, pytest -> -q --tb=line, etc. 10 safe rules,
guarded against composition operators and explicit verbose flags.get_usage_stats v2. ASCII sparkline (30 d), daily breakdown table
(7 d), top-tools cumulative, format="json".ts_discover. Scans ~/.claude/projects/*/*.jsonl
transcripts and flags missed TS opportunities (Read->Grep->Read chains,
sequential find_symbol, edits without get_edit_context,
memory_search without prior memory_index, native shell on code
files). 30-day scan in ~2.5 s on a 343 MB transcript dir.pip install "token-savior-recall[mcp]"
Add to your MCP config (e.g. Claude Code):
{
"mcpServers": {
"token-savior-recall": {
"command": "/path/to/venv/bin/token-savior",
"env": {
"WORKSPACE_ROOTS": "/path/to/project1,/path/to/project2",
"TOKEN_SAVIOR_CLIENT": "claude-code",
"TOKEN_SAVIOR_PROFILE": "optimized"
}
}
}
}
That's it. TOKEN_SAVIOR_PROFILE=optimized ships the Pareto-optimum
config that wins tsbench. It bundles:
tiny_plus (15 hot tools manifest)No other tuning needed.
Bash compaction and the PreToolUse rewriter are opt-in. Two env vars and one CLI call:
export TS_BASH_COMPACT=1 # PostToolUse output compactors (34 of them)
export TS_BASH_REWRITE=1 # PreToolUse command rewriter (10 rules)
ts init --agent claude --yes # auto-merge hooks into ~/.claude/settings.json
ts init is idempotent. It detects existing hook entries, dedups by
(matcher, command), prints a unified diff, and backs up settings.json
to .bak-YYYYMMDD-HHMMSS (UTC) before writing. Supported agents:
claude, cursor, gemini, codex. Pass --dry-run to preview, or
--global to write the user-level config.
Optional audit log of every rewrite:
export TS_BASH_REWRITE_LOG=$HOME/.local/state/token-savior/rewrites.jsonl
| Family | Compactors |
|---|---|
| git | status, diff, log, push/pull, commit, add, fetch, checkout, branch, worktree list, stash list |
| gh | run list, run view, pr diff, pr view, issue view, repo view |
| test/lint | pytest, jest, vitest, eslint, biome, cargo test, cargo build/clippy, tsc |
| cloud | kubectl get, kubectl logs, aws sts, aws ec2, aws lambda, aws logs, aws iam, aws dynamodb, aws s3 |
| docker | docker ps, docker logs |
| packaging | npm/yarn/pnpm list, pip list/show |
| shell catch-alls | grep, find, cat, curl |
Each compactor is a pure function (no I/O, no globals) returning a
token-efficient rendering. The dispatcher returns None when no matcher
fires, leaving the existing sandbox path untouched. Compound commands
(cd ... && cmd) fall through to the last meaningful segment.
ts_discover -- find missed TS opportunitiesNew MCP tool that scans your Claude Code transcripts for patterns where TS tools would have been cheaper than what the agent actually did.
ts_discover() # active project, last 30 days
ts_discover(project=None) # ALL transcript projects
ts_discover(format="adoption") # TS vs native ratio per session
ts_discover(format="adoption_json") # same, JSON
Findings: Read->Grep->Read chains, sequential find_symbol, edits
without get_edit_context, memory_search without memory_index,
native shell on code files. Args are pruned to load-bearing keys
(PII-safe). Streams JSONL with mtime fast-skip.
ts init CLIts init --agent claude [--global] [--dry-run] [--yes]
ts init --agent cursor
ts init --agent gemini
ts init --agent codex
Detects the target agent's settings location, deep-merges the Token
Savior hook config (PostToolUse + PreToolUse), preserves existing
hooks, dedups, prints a unified diff. Backs up to
settings.json.bak-YYYYMMDD-HHMMSS (UTC). Re-running is a no-op.
Claude Code reads whole files to answer questions about three lines, and forgets everything the moment a session ends. Token Savior fixes both, plus a third axis: it now compacts the noisy Bash output that bloats turn budgets between code reads.
It indexes your codebase by symbol -- functions, classes, imports, call
graph -- so the model navigates by pointer instead of by cat. Measured
reduction: 97% fewer chars injected across 170+ real sessions.
On top of that sits a persistent memory engine. Every decision, bugfix, convention, guardrail and session rollup is stored in SQLite WAL + FTS5
And on top of that, since v4.1, sit the Bash compactors and the PreToolUse rewriter. Bench numbers above.
| Profile | Tools exposed | Manifest tokens | When to use |
|---|---|---|---|
optimized |
15 | ~1.5 KT | Recommended default -- Pareto win on tsbench |
auto |
adaptive | ~1-2 KT | Per-client telemetry-based (experimental) |
tiny |
6 | ~0.6 KT | Minimal hot loop |
lean |
51 | ~4 KT | Legacy -- broader surface |
full |
68 | ~6 KT | Everything exposed |
You probably want optimized.
| Operation | Plain Claude | Token Savior | Reduction |
|---|---|---|---|
find_symbol("send_message") |
41M chars (full read) | 67 chars | -99.9% |
get_function_source("compile") |
grep + cat chain | 4.5K chars | direct |
get_change_impact("LLMClient") |
impossible | 16K chars | new capability |
| 96-task tsbench (Opus, plain vs ts) | 17 221 active/task | 3 395 active/task | -80% |
| 7-day Bash output bench (v4.3) | ~30 K tokens/week | ~9.6 K tokens/week | ~20.4 K/week |
pip install "token-savior-recall[mcp]"
# Optional hybrid vector search:
pip install "token-savior-recall[mcp,memory-vector]"
uvx token-savior-recall
claude mcp add token-savior -- /path/to/venv/bin/token-savior
git clone https://github.com/Mibayy/token-savior
cd token-savior
python3 -m venv .venv
.venv/bin/pip install -e ".[mcp,dev]"
pytest tests/ -q
Suite size: 1688 passed, 55 skipped on main. CI green on Python 3.11 / 3.12 / 3.13.
The compactor numbers above come from replaying real Claude Code
transcripts through the dispatcher. Two scripts live under scripts/:
python3 scripts/bench_compactors_real.py # match rate + mean savings
python3 scripts/bench_compactors_unmatched.py # top unmatched commands
The first walks ~/.claude/projects/*/*.jsonl, replays every Bash
output through the registry, and reports per-family savings + overall
match rate. The second buckets the unmatched commands so the next
compactor target is obvious from the histogram.
To reproduce the tsbench score:
git clone https://github.com/Mibayy/tsbench && cd tsbench
python3 generate.py --seed 42
git tag v1
python3 breaking_changes.py
git tag v2
TS_PROFILE=tiny_plus TS_CAPTURE_DISABLED=1 python3 bench.py --tasks all --run B
ts CLI for non-MCP agentsFor agents without MCP (Cursor, Aider, Continue, scripts, CI), the ts
command exposes a subset of the tools via shell:
ts use /path/to/project
ts get my_function # JSON output
ts search 'pattern'
ts daemon start # ~145ms per call vs 1.5s cold fork
ts init --agent cursor # wire up Bash hooks for non-Claude agents
On Claude Code, prefer the MCP server -- measured cheaper than CLI on Opus 4.7. The CLI is there for the portability case.
| Var | Purpose |
|---|---|
TS_BASH_COMPACT=1 |
Enable PostToolUse Bash output compactors |
TS_BASH_REWRITE=1 |
Enable PreToolUse Bash command rewriter |
TS_BASH_REWRITE_LOG |
JSONL audit log of every rewrite |
TS_COMPACT_INLINE_THRESHOLD |
Hybrid mode threshold (default 4 KB) |
TS_COMPACT_TINY_THRESHOLD |
Skip-sandbox threshold (default 256 B) |
TELEGRAM_BOT_TOKEN + TELEGRAM_CHAT_ID |
Critical-observation feed |
TS_VIEWER_PORT |
Web viewer dashboard |
TS_AUTO_EXTRACT=1 + TS_API_KEY |
LLM auto-extraction of memory observations |
TS_CAPTURE_DISABLED=1 |
Skip read-side capture sandboxing (default in optimized) |
TS_MEMORY_DISABLE=1 |
Silence memory hooks (clean-context workloads) |
MIT
Выполни в терминале:
claude mcp add mibayy-token-savior -- npx pro-tip
Поставил Mibayy/token-savior? Скажи Claude: «запомни почему я установил Mibayy/token-savior и что хочу попробовать» — попадёт в твой Vault.
как это работает →Безопасность
Низкий рискАвтоматическая эвристика по публичным данным — не гарантия безопасности.