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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Tool Guardian

作者:LuminariSoftwares版本 0.10.1i★ –i在你的电脑上运行
注册名称io.github.LuminariSoftwares/tool-guardian

安装

概览

作者提供的说明(英文原文)

Too many MCP servers eating your context? A router that loads each tool's schema only when needed.

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)。
  • 注册中心没有列出需要填写的密钥。

你需要

  • PyPI已安装 Python 的 uv(用于 uvx 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": {
    "tool-guardian": {
      "command": "uvx",
      "args": [
        "tool-guardian"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add tool-guardian -- uvx tool-guardian

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": {
    "tool-guardian": {
      "command": "uvx",
      "args": [
        "tool-guardian"
      ]
    }
  }
}

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": {
    "tool-guardian": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "tool-guardian"
      ]
    }
  }
}

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。

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8日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 LuminariSoftwares 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 18486 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 18486 个服务器的 98% 更新
  • 基础检查结果等于或优于此分类中其他 18486 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

Tool Guardian 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(0.10.1)于昨天进入注册中心。

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 Python (uv)。

有什么可以替代它?

同一分类中有:Spec Driven Development, Remote Desktop Commander, Compose Preview Catalogs

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:↓ 每周下载量i★ 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

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

作者:formulahendry起步阶段↓ 66/周★ 44014个月前更新

在你的电脑上运行

Remote Desktop Commander

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

作者:desktop-commander正在增长★ 2113天前更新

在云端运行✓ desktopcommander.app

Compose Preview Catalogs

Browse, inspect and render Jetpack Compose Material 3 and Wear component catalogs.

作者:yschimke非常新今天更新

在云端运行

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。

Tool Guardian

Status: active development -- changelog

mcp-name: io.github.LuminariSoftwares/tool-guardian

Seven MCP servers for 1,084 tokens instead of 20,005.

One MCP server in front of all your others, so the model fetches a tool schema only when it needs one.

Quick start (60 seconds)

Nothing to install: uvx runs it from PyPI. The example server below is the MCP filesystem server, which runs with npx, so that one line needs Node.js — the router itself is Python 3.9+ and standard library only.

uvx tool-guardian --version

uvx --from tool-guardian tool-guardian-setup add files -- npx -y @modelcontextprotocol/server-filesystem "$HOME" uvx tool-guardian --selftest claude mcp add --scope user tool-guardian -- uvx tool-guardian # or any client: see "Run" below

Or tell your coding agent: Install tool-guardian (docs: https://raw.githubusercontent.com/LuminariSoftwares/tool-guardian/main/llms.txt) and route my MCP servers through it.

Prefer it installed? pip install tool-guardian, then drop the uvx prefixes (tool-guardian-setup add ..., tool-guardian --selftest).

Success looks like added 'files' to ~/.tool-guardian/mcp.json, then from --selftest 1 backend(s) up, 14 tools reachable. and a router tools cost ~N tokens vs ~M line. Already have servers in Claude Desktop, Cursor or Windsurf? tool-guardian-setup import copies them instead of add. In the client, ask for something one of your servers does; the model should call list_capabilities (or search_capabilities) and then call_tool.

What it looks like in practice

A real session against two servers (files and git), abridged only where marked. The model asks for the catalogue, narrows to one server, checks one tool's arguments, then calls it:

> list_capabilities()

[files] 14 tools: read_file, read_text_file, read_media_file, read_multiple_files, write_file, edit_file, create_directory, list_directory, list_directory_with_sizes, directory_tree, move_file, search_files, get_file_info, list_allowed_directories [git] 12 tools: git_status, git_diff_unstaged, git_diff_staged, git_diff, git_commit, git_add, git_reset, git_log, git_create_branch, git_checkout, git_show, git_branch

NEXT STEP: use search_capabilities with a keyword (or list_capabilities with one server name) to see a tool's args, then call_tool.

list_capabilities(server="files")

files.read_file: Read the complete contents of a file as text. DEPRECATED: Use read_text_file... args: path:string, ?tail:number, ?head:number files.read_text_file: Read the complete contents of a file from the file system as... args: path:string, ?tail:number, ?head:number ... 11 more tools ... files.list_allowed_directories: Returns the list of directories that this server is allowed to access.... args: (no arguments)

NEXT STEP: you have not answered the user yet. Pick the tool above that does the job and use call_tool with server files, that tool's name, and the args listed under it.

describe_tool(server="files", tool="read_text_file")

files.read_text_file: Read the complete contents of a file from the file system as... required args: path:string

NEXT STEP: use call_tool with server files, tool read_text_file, args: path:string, ?tail:number, ?head:number.

call_tool(server="files", tool="read_text_file", args={"path": "project/README.txt"})

hello from tool-guardian

NEXT STEP: answer the user from this result.

Only the router's own six tools were in the model's context the whole time; the 26 backend tools were never sent.

Proof

The result line is this repo's bench, run against the recorded tools/list answers of seven public MCP servers (no network, no model; abridged where marked):

$ python bench/bench_tokens.py

Tool-Guardian Token Benchmark

Token counting method: estimate:chars/3.5

| server | tools | tokens (full schemas) | ... | Total | 95 | 20005 |

Every request: 20005 tokens of tool schemas without tool-guardian, 1084 with it (94.6% less).

<!-- c1 --><!-- c2 -->

Run 2026-11-05 on the v0.10.0 tag. Test suite (pip install -r requirements-dev.txt && python -m pytest -q), run 2026-11-05:

428 passed, 4 skipped in 85.82s (0:01:25)

--selftest proves the *saving* and that your backends start — it does not prove your model will drive the router in your harness. See Model requirement.

One MCP server in front of all your others. Tested with Claude Code, OpenCode, Cline and Continue (one scripted-model turn per client, see Works with ); Claude Desktop, Cursor and other stdio MCP clients: expected, not yet tested.

Quick start &middot; Works with &middot; DeepSeek Harness &middot; Install both &middot; First 5 minutes &middot; Why &middot; Two ways to run it &middot; Architecture &middot; Install &middot; Measure it &middot; Output ladder &middot; DSH bundle &middot; Compatibility &middot; Changelog

An MCP server that sits in front of your other MCP servers and exposes 6 router tools (8 with skills) instead of dozens of specific ones: four generic ones to list, search, describe and call the tools behind it, plus two for tool-group costs and archived results (see Tools) — discovering the rest on demand — so tool definitions stop eating your context window before the model reads a word.

The router never talks to the model backend, so it does not matter whether Ollama, LM Studio, vLLM or llama.cpp serves your model; what matters is that your client's model calls tools (run with Ollama; the others expected, not yet tested).

Companion to Context Guardian: Context Guardian compacts the conversation before the window fills; Tool Guardian keeps the tools from filling it in the first place. Two halves of the same problem.

Without Tool GuardianWith it
7 MCP servers on a 32K model (bench)20,005 tokens of schemas on every request (61 % of the window)1,084 tokens (3 %); a schema is fetched only when the model asks
A 3,000-line build log with one error (bench)115,945 characters land in the conversation4,540 characters, the full original archived and one call away
A backend that fails to startan empty tool list the model silently works aroundstatus UNKNOWN plus the real error (e.g. could not start: …), never an empty list

When *not* to use the router

It is the wrong tool in two cases, and saying so is cheaper than finding out:

  • Your client already searches tools for you. Some clients (and some model APIs) load tool definitions on demand themselves. Putting a router under that adds a hop and a second catalogue for no saving.
  • You run a handful of small servers. The router's own tools cost about 1,100 tokens of tools/list; one small server behind it is close to break-even (--selftest prints the real arithmetic for *your* servers before you commit to it). The saving grows with every server added and is largest where it matters most — a 32K local model with half a dozen servers.

It also does not help if your model will not call tools reliably in the first place: see Model requirement.

Works with

pip install tool-guardian, then one entry in your client. Every example below starts the same command, tool-guardian, which reads ~/.tool-guardian/mcp.json: tool-guardian-setup import fills it from the servers you already use.

ClientExampleWhere it goesTested
Claude Codeproject.mcp.json, hook: settings.json, notesclaude mcp add --scope user tool-guardian -- uvx tool-guardian, or copy to .mcp.json in a project (Claude Code asks once to approve a project server — that file is the client's config, not the router's) ; the hook goes in ~/.claude/settings.json or .claude/settings.json2.1.296: full turn; hook in a live session
OpenCodeopencode.jsonopencode.json in the project, or ~/.config/opencode/opencode.json1.18.35: full turn
Clinecline_mcp_settings.jsonVS Code: Cline &rarr; MCP Servers &rarr; Configure MCP Servers; CLI: ~/.cline/data/settings/cline_mcp_settings.jsonCLI 3.0.70: full turn
Continuetool-guardian.yaml.continue/mcpServers/ in the workspace (IDE), or its mcpServers entry in config.yamlcn 1.5.47: full turn (entry in config.yaml)

What "full turn" means. On Linux, 2026-10-10, each client got the example's tool-guardian entry unchanged (Claude Code through claude mcp add-json; OpenCode and Continue with a test model provider added beside it; Cline the file as is) and talked to a scripted model (tests/clients/mock_model.py, no real model, no API key). The client sent tool-guardian's router tools to the model. The model's list_capabilities call went through tool-guardian to a stub MCP server, and the answer came back to the model. tool-guardian's call log recorded the client by name (claude-code, opencode, @cline/core, continue-cli-client). Re-run it: pip install -e . then python tests/clients/run_clients.py. This proves the wiring, not that *your* model will use the router: see Model requirement.

The Claude Code hook in a live session. The hook command from settings.json with --mode deny added (wrapped to

只显示了开头部分;其余内容请在仓库中阅读

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