shadow-mcp

Name: shadow-mcp
Author: saagpatel

by saagpatelversion 0.2.0i★ –iRuns on your computer

Registry nameio.github.saagpatel/shadow-mcp

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

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

Run this in your terminal

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

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account saagpatel, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 32464 servers in this category
Published to the registry more recently than 15% of the other 32464 servers in this category
Basic checks equal to or better than 100% of the other 32464 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is shadow-mcp safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.2.0) reached the registry 4 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, yontu

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Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

yontu

README from the maker

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