原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
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Token Optimizer MCP

作者:ooples版本 7.9.0i★ –i在你的电脑上运行
注册名称io.github.ooples/token-optimizer-mcp

安装

概览

作者提供的说明(英文原文)

Context-window optimization: caching, compression & smart file tools that cut tokens 60-90%.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 其他

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 需要你自己填写: TOKEN_OPTIMIZER_CACHE_DIR

你需要

  • npm已安装 Node.js(用于 npx 命令)

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "token-optimizer": {
      "command": "npx",
      "args": [
        "-y",
        "@ooples/token-optimizer-mcp"
      ],
      "env": {
        "TOKEN_OPTIMIZER_CACHE_DIR": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add token-optimizer --env TOKEN_OPTIMIZER_CACHE_DIR=your-value -- npx -y @ooples/token-optimizer-mcp

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "token-optimizer": {
      "command": "npx",
      "args": [
        "-y",
        "@ooples/token-optimizer-mcp"
      ],
      "env": {
        "TOKEN_OPTIMIZER_CACHE_DIR": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "token-optimizer": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@ooples/token-optimizer-mcp"
      ],
      "env": {
        "TOKEN_OPTIMIZER_CACHE_DIR": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

需要你填写的值

  • TOKEN_OPTIMIZER_CACHE_DIR env
    Directory path for cache storage (optional, defaults to ~/.token-optimizer-cache)

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 ooples 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

与“其他”中的其他服务器比较i

  • 星标数多于此分类中其他 32464 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 32464 个服务器的 94% 更新
  • 基础检查结果等于或优于此分类中其他 32464 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

Token Optimizer MCP 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(7.9.0)于前天进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。

有什么可以替代它?

同一分类中有:Spec Driven Development, Remote Desktop Commander, yontu

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Spec Driven Development

作者:formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

在你的电脑上运行★ 440MIT14个月前更新
安装

Remote Desktop Commander

作者:desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

在云端运行★ 211昨天更新
安装

yontu

作者:senrecep

Türkçe yazı denetimi: TDK kuralları, yapay zekâ izleri, metin türüne göre üslup.

在你的电脑上运行今天更新
安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

Token Optimizer MCP

Compression that optimises your bill, not your byte count — and ships the benchmark so you can check it.

One local ledger for optimizer tools, live-graph substitutions, every agent, and the graph's own cost.


How it compares to Alternative A

Providers cache the prompt prefix: cached tokens re-read at 0.1x, rewritten ones bill at 1.25x. Most compressors optimise bytes removed and ignore that multiplier. This one optimises the bill.

Measured against their real implementation, on their own fixtures. Not a reimplementation and not our fixtures: their harness compresses the payload, dumps it, their resolver redeems their own markers, and ours is handed the identical bytes. Their column is the best of the nine configurations that capture holds, row by row.

Two of our arms are in the table, and they answer different questions. `ours` is the block engine with nowhere to move a block, which is how the MCP tools and the hooks run: everything it removes, it removes by compressing, and nothing leaves the request. `ours, proxy` is the proxy a Claude Code session runs through, which also moves a block it could not compress out to a file and leaves a marker naming the path -- the same kind of trade their content-cache reference makes. ours, preset is the block engine with that move switched on at spillWholeBlockBelow: 0.9.

workloadpayloadtheirsoursours, proxyours, presetzero-turn ids
agentic-conversation244,90837.3% / 33.4%42.9% / 30.8%52.4% / 49.0%100.0% / 100.0%2099 / 2099 / 0
agent-loop170,93842.2% / 54.3%69.3% / 59.1%92.0% / 94.1%93.6% / 94.3%185 / 185 / 52
agent-loop-logs327,50051.5% / 53.0%61.9% / 58.1%88.7% / 91.5%96.2% / 97.0%2034 / 2034 / 81
api-responses49,12788.2% / 89.3%66.6% / 59.1%96.4% / 96.8%96.6% / 96.8%699 / 98 / 23
browser-session781,77421.7% / 13.1%93.7% / 26.5%55.5% / 90.2%100.0% / 99.9%334 / 334 / 0
code-search131,26447.4% / 59.7%81.8% / 70.8%97.7% / 97.9%95.7% / 96.1%201 / 201 / 43
database-rows45,92988.6% / 89.2%65.6% / 56.5%92.4% / 93.1%92.6% / 93.4%590 / 78 / 49
codebase-exploration137,96199.7% / 99.7%16.0% / 12.7%99.6% / 99.6%100.0% / 99.9%540 / 3 / 0
grep-output75,21999.6% / 99.6%56.1% / 51.9%99.5% / 99.6%99.9% / 99.9%1005 / 3 / 0
log-entries91,38580.8% / 83.2%63.5% / 52.0%95.4% / 95.7%95.5% / 96.0%1180 / 237 / 46
human-authored-json37,42530.6% / 30.0%66.6% / 66.0%81.6% / 83.7%96.7% / 96.9%154 / 154 / 11
issue-triage109,35453.1% / 55.2%88.1% / 85.7%97.2% / 97.6%95.1% / 95.7%59 / 59 / 21
rag-conversation171,9130.0% / 0.1%84.6% / 83.2%99.1% / 99.4%100.0% / 100.0%42 / 42 / 0
raw-build-log158,05799.8% / 99.8%70.6% / 48.2%99.7% / 99.8%100.0% / 100.0%430 / 3 / 0
relevance-probe49,15764.1% / 71.0%82.4% / 77.2%97.1% / 97.8%95.8% / 96.6%225 / 225 / 13
search-results96,64795.0% / 95.4%74.3% / 59.4%96.5% / 96.5%96.6% / 96.6%505 / 62 / 34
repeated-reads158,28537.4% / 30.4%71.7% / 68.5%99.5% / 99.6%100.0% / 100.0%394 / 388 / 0
sre-debugging312,27688.4% / 89.9%67.4% / 62.4%98.5% / 98.7%97.1% / 97.5%3116 / 506 / 82

Recorded 2026-10-17 at b1231a8c, by the command in the record's regenerate field, over Alternative A's own fixtures and harness. That arm runs Alternative A itself, so CI does not re-derive it -- it checks these figures and this provenance against the record instead.

Over the corpus the proxy removes 82.5% of the characters and 91.5% of the tokens; their best configuration removes 51.5% and 61.4%. It is cheaper than their best arm on 18 of 18 rows when nothing is ever fetched back, which is the end of the range their markers are built for, and on 17 of 18 when every moved block is fetched back. The block engine alone removes 72.0% and 55.9%: more of the characters than theirs, fewer of the tokens.

Where they still win, and why. On codebase-exploration, grep-output, raw-build-log, sre-debugging and the four raw-data rows their best arm takes 83.2-99.8% off the tokens and the block engine 12.7-62.4%. Their number there is a content-cache reference: the block is not made smaller, it is taken out of the request, put in a store, and replaced by a < > marker. An engine with nowhere to put a block cannot match that, so on those rows the ours column loses on reduction, and on cost when nothing is fetched back. The proxy makes the same move: it is ahead of them on five of those eight rows, level on grep-output and raw-build-log, and a tenth of a point behind on codebase-exploration. The one row where the proxy costs more than they do is agentic-conversation, and only when every block it moved is fetched back: they move none of that conversation out, so they cost the same at either end, while the proxy moves part of it and pays a retrieval for each part it wants back.

The other column is a turn. A marker is not the content: to read what it stands for, the agent spends a request. Of the 13,792 retention units planted in the corpus, the block engine keeps 5,223 in the text and 8,569 more rebuildable from the text alone, so every one of them is there with no extra turn; theirs keeps 6,711 in the text and 7,075 behind its store, one retrieval call away. 0 are unrecoverable on our side and 6 on theirs. Their best arms force 17 round trips across the corpus to get their content back; the block engine forces 0. The proxy, like theirs, trades a turn for every block it moves, and that turn is what the fetch-rate end of the cost model prices.

What their marker costs is priced, not just its size. Their marker is only redeemable because their proxy also sends a alternative_a_retrieve tool with the request -- on by default (ccr_inject_tool), and on every later request once a session has used it -- so every row where their best arm leaves a marker is charged for that tool's definition as well, recorded from their own package by bench/compression/alt-a/ccr-tool.py. The turn that first adds the tool also rewrites the whole cached prefix behind it; that is not charged, which errs in their favour. Ours needs no tool: our marker names a path the agent opens with the Read it already has, and the path is priced at the length the proxy really writes ( /.tok/ /<15 digits>, src/compress/spill-path.ts) rather than a shorter invented one.

`browser-session` is mostly the fixture. This payload holds four images, two of them distinct, and the base64 in them is generated rather than photographic: one distinct image alone folds from 160,032 characters to 5,572, which no real PNG would do. What carries over to a real session is the duplication -- half the image bytes are a second copy of an image already in the request, which is 44.3% of the whole payload. The run the marker names is still in the output above it, so the reader rebuilds it without asking for anything, which is why the characters fall much further than the tokens: the image tokens are counted from pixels on both arms either way.

Reproduce the whole table:

python bench/compression/alt-a/run-theirs.py <alt-a-clone> <out-dir>

python bench/compression/alt-a/resolve-theirs.py node bench/compression/head-to-head.mjs

Against their published design

A second arm reimplements their published design from their own benchmark generator's definitions — opaque hash markers, history compressed, a retrieval tool and system message injected — so the four workloads with a published comparator can be checked without their clone. node bench/compression/proof.mjs:

workloadpayloadtheirsoursverdict
code-search1776592.1%97.8%ours
sre-debugging6569492.2%98.6%ours
issue-triage5417472.8%97.5%ours
codebase-exploration7850247.4%54.7%ours

Four workloads, because those are the four this arm has a published comparator for. The head-to-head harness scores eighteen; the other fourteen are not in this arm, and are scored against their real implementation in the table above.

**`codebase

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