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  3. Token Savior
Token Savior logo
Health: ActiveRecent health check succeeded.Last checked 9/9/2026, 6:34:54 PM

Token Savior

User RatingsBe the first to rate and review this MCP server!
View Repository1.1k GitHub StarsTotal stargazers on GitHub for the source repository (1,146 stars).Visit Website
developer-toolscode-intelligencenavigationtoken-optimizationbash

Structural code intelligence MCP server offering symbol-based navigation, dependency graphs, call chains, and token-efficient code analysis.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent โ€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag โ€” we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON โ–พ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "mibayy-token-savior": {
      "command": "uvx",
      "args": [
        "token-savior-recall"
      ]
    }
  }
}

๐Ÿ’ก Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing Alternatives๐Ÿ’ฐ More in Finance & Fintech

Overview

This MCP server enables AI-assisted development by providing structural code navigation and editing based on symbol names rather than raw text. It supports features like symbol lookup, dependency graphs, call chains, dead code detection, and structural diffs, significantly reducing token usage on large codebases. It also offers optional Bash output compaction and command rewriting to optimize shell interactions for AI agents. Use it to improve code understanding and editing efficiency in AI coding workflows.

Use cases

โ€ขNavigate code by symbol names instead of raw text
โ€ขAnalyze dependency graphs and call chains in codebases
โ€ขDetect dead code and perform structural diffs
โ€ขCompact and rewrite Bash command outputs for AI agents
โ€ขAudit AI code interaction transcripts for optimization opportunities

Key features

โ€ขSymbol-based code navigation and editing
โ€ขDependency graph and call chain visualization
โ€ขDead code detection
โ€ขStructural diff of code changes
โ€ขBash output compaction with 34 specialized compactors
โ€ขPreToolUse command rewriting with 10 rules

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Token Savior.

Extracted Tool Capabilities
Symbol-based code navigation and editing
Dependency graph and call chain visualization
Dead code detection
Structural diff of code changes
Bash output compaction with 34 specialized compactors
PreToolUse command rewriting with 10 rules

Documentation Overview

Token Savior

One MCP server. One profile. 97.9% on tsbench at -80% tokens. Structural code navigation, persistent memory, and Bash command rewriting for AI coding agents.

PyPI Benchmark Python 3.11+ MCP CI

mibayy.github.io/token-savior -- project site + benchmark landing Benchmark source + fixtures: not currently published (see Reproducing the score below)


Benchmark -- 96 real coding tasks (Claude Opus 4.7, May 2026)

Plain Claude CodeWith Token Savior
Score141 / 180 (78.3%)188 / 192 (97.9%)
Active tokens / task17 2213 395 (-80%)
Wall time / task110.6 s18.9 s (-83%)

Reproduces with the optimized profile (single env var). The harness that produced these numbers is described below; its repository is not public at the moment, so take the figures as reported rather than as independently verifiable.

A re-measurement was published here on 2026-08-09 and has been withdrawn on 2026-08-10. It reported new-token savings from a small replacement harness. Those numbers did not measure this server at all: across 143 benchmark sessions, exactly one called a Token Savior tool. The client running the harness had MCP deferred-tool loading enabled, so all 18 tools sat behind a ToolSearch lookup instead of appearing in the model's manifest. The model never saw them and fell back to Grep and Read โ€” 66 greps, 30 reads, one MCP call. What varied between the "profiles" was the size of the cached prefix, not what the agent did.

The lesson is worth more than the numbers were: a benchmark of an MCP server must assert that its tools were actually called. Ours did not, so it happily compared two identical agents. That assertion now exists in the harness.

The headline figures above therefore stand as reported and unverified, as stated in the previous paragraph. Re-measuring them properly is open work.


Who starred this repo?

starscope

On June 30, 2026 GitHub restricted stargazer and watcher lists to repo admins and collaborators, which broke every "who starred my repo" tool at once. I rebuilt one that still works, precisely because it only reads repos you own or can push to: starscope ranks the people who starred or forked your repo by influence, and surfaces their social accounts when their GitHub profile declares them.

Numbers on this very repo, computed with it: 1,147 people, 27% with a public social account, and the most followed carries 18,922 followers. The named list is visible to the repo owner and to nobody else โ€” the public page shows aggregates only.


What's new

Release notes live where they can't drift out of sync with the code:

  • CHANGELOG.md for the full history
  • Releases for the tagged builds
  • PyPI for what pip will actually give you

Quick start

Terminal
pip install "token-savior-recall[mcp]"

Add to your MCP config (e.g. Claude Code):

config.json
{
  "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)
  • thin inputSchema (-44% manifest)
  • capture sandbox disabled
  • memory hooks gated for cross-project safety

No other tuning needed.


Activation (Bash compaction + rewriting)

Bash compaction and the PreToolUse rewriter are opt-in. Two env vars and one CLI call:

server.ts
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, openclaw. Pass --dry-run to preview, or --global to write the user-level config.

Optional audit log of every rewrite:

server.ts
export TS_BASH_REWRITE_LOG=$HOME/.local/state/token-savior/rewrites.jsonl

Compactor catalog (34)

FamilyCompactors
gitstatus, diff, log, push/pull, commit, add, fetch, checkout, branch, worktree list, stash list
ghrun list, run view, pr diff, pr view, issue view, repo view
test/lintpytest, jest, vitest, eslint, biome, cargo test, cargo build/clippy, tsc
cloudkubectl get, kubectl logs, aws sts, aws ec2, aws lambda, aws logs, aws iam, aws dynamodb, aws s3
dockerdocker ps, docker logs
packagingnpm/yarn/pnpm list, pip list/show
shell catch-allsgrep, 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.

These run in PostToolUse, so they do not shrink the current turn. The hook fires after the tool has returned; it can add context, not remove it. The compact rendering is appended below the raw output, which stays. What you gain is persistence: the full output goes to the capture sandbox and outlives a context compaction. For an actual reduction of what reaches the model, use the PreToolUse rewriter (TS_BASH_REWRITE=1) โ€” it edits the command before it runs.


ts_discover -- find missed TS opportunities

New MCP tool that scans your Claude Code transcripts for patterns where TS tools would have been cheaper than what the agent actually did.

python
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 CLI

bash
ts init --agent claude [--global] [--dry-run] [--yes]
ts init --agent cursor
ts init --agent gemini
ts init --agent codex
ts init --agent openclaw

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.


What it does

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 bounds the noisy Bash output that bloats turn budgets between code reads โ€” by rewriting the command before it runs.

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

  • vector embeddings, ranked by Bayesian validity and ROI, and re-injected as a compact delta at the start of the next session.

And on top of that, since v4.1, sit the Bash compactors and the PreToolUse rewriter. Bench numbers above.

Which of those two actually shrinks a turn, and which does not. Measured 2026-08-09, and worth stating plainly because the distinction is not obvious:

  • The PreToolUse rewriter changes the command before it runs, so a smaller output is produced and a smaller output reaches the model. This is a real reduction in the current turn.
  • The PostToolUse compactors run after the tool has returned. A PostToolUse hook can only add context; by the time it fires, the raw output has already been sent. The compact rendering is appended, it does not replace anything. What the compactors genuinely buy you is different and still valuable: the full output is preserved in the capture sandbox and survives a context compaction, so it can be queried later instead of being re-run.

If your goal is a smaller turn, TS_BASH_REWRITE=1 is the switch that does it. If your goal is to stop losing command output across compactions, that is TS_BASH_COMPACT=1.


Profile comparison

Read the full README โ†’View source on GitHub โ†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks โ€” not a rating.

GitHub stars
1.1k
Stargazers on the source repository.
Directory activity
2 views
Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet โ€” be the first to share how this listing worked for you.

Frequently Asked Questions about Token Savior

Install via pip with 'pip install "token-savior-recall[mcp]"' and configure your MCP client to run the 'token-savior' command with appropriate environment variables.

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Technical Specs & Signals

Category๐Ÿ’ฐFinance & Fintech
PricingFree
More technical detailsExpand โ–พ
TransportSTDIO
RuntimePython
AuthNo auth required
ClientsClaude Desktop, Cursor
Last updatedAug 9, 2026
Views2
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars1,146
GitHub Star CountTotal stargazers on GitHub representing community popularity (1,146 stars).
56Quality signal: Good ยท 56/100How this signal is calculated โ–พ
Server availabilityNot measured

Not scored for repo-hosted servers โ€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools25/30
Adoption & activity7/15
Community engagement0/10

A guidance signal from public completeness & health data โ€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 22d ago via OSV.dev ยท token-savior-recall (PyPI)

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