shadow-mcp

Name: shadow-mcp
Author: saagpatel

作者：saagpatel版本 0.2.0i★ –i在你的电脑上运行

注册名称io.github.saagpatel/shadow-mcp

安装

概览

作者提供的说明（英文原文）

Local MCP server that inventories and risk-grades MCP servers configured on this machine.

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。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

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？

ClaudeClaude CodeCursorVS CodeChatGPT

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": {
    "shadow": {
      "command": "uvx",
      "args": [
        "shadow-mcp",
        "mcp-serve"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add shadow -- uvx shadow-mcp mcp-serve

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": {
    "shadow": {
      "command": "uvx",
      "args": [
        "shadow-mcp",
        "mcp-serve"
      ]
    }
  }
}

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": {
    "shadow": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "shadow-mcp",
        "mcp-serve"
      ]
    }
  }
}

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6日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 saagpatel 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 32455 个服务器的 0%
发布到注册中心的时间比此分类中其他 32455 个服务器的 15% 更新
基础检查结果等于或优于此分类中其他 32455 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

shadow-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（0.2.0）于4个月前进入注册中心。

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, SSTracker free youth training

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

SSTracker free youth training

作者：sstracker.app

Free youth sports drills, practice plans and age-group answers for parents and coaches.

在云端运行今天更新

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。

shadow-mcp

Discover and risk-grade the MCP servers actually present on this machine.

Most MCP security tooling assumes you already have a list of servers to audit. On a real developer machine you don't: servers are scattered across Claude Code, Codex, Claude Desktop, project-local .mcp.json files, DXT extensions, and live processes that bind no port. shadow-mcp finds them first, then grades them.

This is the local-first answer to OWASP MCP09:2025 — Shadow MCP Servers.

What it does

discover  ->  inventory  ->  risk-grade  ->  report

Discover (read-only) MCP servers from supported configuration and runtime sources: Claude Code (~/.claude.json, user + project scope), claude mcp list (catches remote + plugin servers no file contains), Codex (~/.codex/config.toml + profiles), project .mcp.json, Claude Desktop config + DXT extension manifests, and the live process table.
Inventory: merge sightings into one entry per logical server, even when a server appears under different names across hosts (personal-ops vs personal_ops), tracking every provenance.
Risk-grade by delegating to the existing engines rather than reimplementing them:

MCPAudit for a 0-10 capability composite + injection findings
mcp-trust for an authoritative A-F danger grade (when known)
a thin local layer for the config-shaped OWASP dimensions the engines under-cover (secrets/MCP01, supply-chain provenance/MCP04, transport exposure/MCP07).

Report: a ranked terminal table, a machine-readable JSON inventory, or markdown — plus a Shadow & attention section for the deltas that matter (running-but-unconfigured, broad blast radius, capable-but-ungraded).

The risk model and its OWASP mapping live in docs/risk-model.md.

Install

From this repository root, use uv:

make setup

The grading engines (mcp-audits and mcp-trust) are runtime dependencies resolved from PyPI, not editable sibling checkouts or an engines group. The installed mcp-trust package supplies its seed catalog; --registry-db or SHADOW_MCP_MCPTRUST_DB selects an explicitly chosen local registry instead.

Use

uv run shadow-mcp scan                      # full pipeline, terminal report

uv run shadow-mcp scan --json out.json # machine-readable inventory uv run shadow-mcp scan --format markdown # markdown report uv run shadow-mcp discover # inventory only, no grading uv run shadow-mcp sources # per-collector counts uv run shadow-mcp grade-missing # A-F for servers the registry hasn't scanned uv run shadow-mcp deep-scan cost-tracker # connect to a server, grade its real tools

Useful flags: --no-processes (skip the live process scan), --no-cli (skip claude mcp list), --no-mcpaudit (inventory + mcp-trust only), --home PATH (point discovery at a fixture tree).

Static vs connected grading

By default grading is static (config-only): no server is spawned, so grades reflect what's visible in the config. That's safe but coarse — a server's real capability only shows once you connect and list its tools.

shadow-mcp scan --connect (or deep-scan [names...]) spawns each stdio server and enumerates its real tools, delegating to MCPAudit's connected engine for a capability grade that actually differentiates (a filesystem server jumps from a static A to a connected D). This is opt-in because connecting executes the server; remote endpoints are never spawned (that's the network-scan tier), and a server that needs real secrets to start falls back to its static grade.

Development and safe verification

The committed uv.lock fixes the PyPI runtime and development graph. From the repository root, run:

make check

The suite uses synthetic homes. Keep SHADOW_MCP_RUN_CONNECT unset: the explicitly opted-in connected test is a separate server-execution lane. CI runs the same aggregate gate on Python 3.11 through 3.15; see the workflow. make build creates the wheel and sdist used by the release workflow.

Do not use the normal scan, discover, sources, grade-missing or MCP tool calls as a development smoke: they inventory the machine by default. CLI deep-scan and scan --connect additionally execute servers. For changed JSON or Markdown reports, check the corresponding fixture tests (tests/test_report.py) and inspect synthetic output; this CLI has no browser UI. Actual workstation or connected behavior requires its own explicitly authorized qualification.

Safety

Read-only discovery. Collectors parse configs, invoke claude mcp list (unless --no-cli is set), and list processes; nothing they find is ever mutated. (--connect/deep-scan is the one path that *executes* servers, and only when you explicitly ask.)
Secrets stay out. We record env variable *names* (to flag secret-bearing servers per MCP01) but never their values. Inline credentials in command arguments, URLs, and process parent commands are redacted before storage. A captured inventory still contains local paths and hostnames, so treat *.inventory.json as private (it is git-ignored by default).

Use as an MCP server

shadow-mcp can serve its own inventory tools as an MCP server so an agent can query your local MCP surface without leaving the conversation.

Tools

Tool	Description
`scan_local`	Full pipeline (discover → inventory → grade → report). Returns JSON.
`discover_local`	Inventory every MCP server without grading. Returns JSON.
`deep_scan`	Grade the named servers, or all servers when the list is empty (static, no spawning). Accepts `names: list[str]`. Returns JSON.
`list_sources`	Per-collector source counts from a discover run. Returns JSON.

Run the server

# directly from a local checkout

uv run shadow-mcp mcp-serve

via uvx (once published to PyPI)

uvx shadow-mcp mcp-serve

LOCAL only. MCP tools read local configs and the process table, skip claude mcp list, and never connect to hosted MCP endpoints or spawn servers. Grading is static (config-based); connect=False is enforced unconditionally.

Scope

This is the local-first tool: it inventories one machine from its configs and processes. A later network-scan expansion (probing hosts/ports for remote MCP endpoints, org-wide fleet inventory, typosquat-distance provenance checks) is deliberately out of scope here — see the bottom of docs/risk-model.md and the project notes for what that would add.

Status

Stack

Python 3.11+; uv-managed locked dependencies; Ruff, mypy, and pytest checks; Hatchling wheel and sdist builds.

Known risks

The minimum Python version remains 3.11 pending an owner decision about raising it to 3.12.
The MCP tool name deep_scan remains unchanged pending an owner decision; the similarly named CLI command can connect to servers.
Identity collisions in identity and inventory matching.
Process sightings are deduplicated by name before owner classification.
Grading evidence is limited to eight findings, and critical ranks below low.
Collector failures can be silent.
--json is ignored by sources and grade-missing.
Report writes are not atomic.
Markdown and Rich output escaping differs.
Dependency major upgrades, moving off Hatchling, a coverage threshold, package publishing, tags, and releases remain out of scope.

Next steps

Resolve the Python floor and MCP tool naming decisions, then address the listed behavior risks in a follow-up wave.

shadow-mcp

概览

PopNx MCP 的三大支柱

它能帮你做什么

安装

安装之前

它能做什么

基础检查i

作者与仓库一致

仍在维护

有许可证

端点有响应

使用 OAuth 登录

工具描述中没有隐藏指令

与“其他”中的其他服务器比较i

常见问题

替代选择

Spec Driven Development

Remote Desktop Commander

SSTracker free youth training

作者的 README

shadow-mcp

What it does

Install

Use

Static vs connected grading

Development and safe verification

Safety

Use as an MCP server

Tools

Run the server

via uvx (once published to PyPI)

Scope

Status

Stack

Known risks

Next steps