About agent-guard-mcp
agent-guard-mcp is an MCP server for agents and teams checking software supply-chain changes before installation or merge. It evaluates packages, lockfiles, skill manifests, and CI workflows through four focused tools.
Key features
- 01Check npm and PyPI packages
- 02Scan direct and transitive lockfile dependencies
- 03Score skill and plugin manifests
- 04Inspect CI workflows for risks
Use cases
- 01Review a dependency before installing it
- 02Check a generated lockfile before running install
- 03Assess a third-party skill or MCP plugin
- 04Review CI workflow changes before merging
MCP support
Exposes four tools—`check_package`, `verify_lockfile`, `score_manifest`, and `check_workflow`—to MCP clients. The same checks are also available through an HTTP service.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
4Saved tool list · updated 2w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| name | string | Yes | Package name, e.g. "huggingface-cli". |
| ecosystem | enum | No | npm | pypi |
Details
From the project READMEagent-guard-mcp
One line: Verify-before-act safety for AI agents — check packages, lockfiles, skill manifests, and CI workflows before install or merge. Zero API keys. Zero config.
Scores Cursor/Claude skill manifests + GitHub Actions workflows — not just npm dependencies.
| When you… | Call |
|---|---|
npm install foo / agent suggests a new dep | check_package |
| Review an LLM-generated lockfile | verify_lockfile |
| Install a Cursor skill or MCP plugin | score_manifest |
Merge a PR touching .github/workflows/* | check_workflow |
Call these before installing a dependency, merging a CI change, or installing a third-party skill/plugin — so an agent refuses hallucinated packages, typosquats, poisoned manifests, and compromised CI actions.
Exposes four MCP tools (also available as a plain HTTP service):
| Tool | Use before… | Returns |
|---|---|---|
check_package | adding a single npm/PyPI dependency | OK / SUSPICIOUS / DANGER + typosquat/slop flags |
verify_lockfile | running npm/pip/yarn install | scans every direct + transitive dep in a lockfile |
score_manifest | installing a Cursor/Claude skill or MCP/Smithery plugin | poison-signature + scope-overreach + drift score 0–100 |
check_workflow | merging a PR that touches CI | flags mutable action pins, compromised actions, curl|bash, pwn-requests, secret exposure |
Why
AI agents routinely hallucinate package names (slopsquatting), trust LLM-generated lockfiles, install unvetted skill packs, and pin CI actions to mutable tags. Each is a live supply-chain vector. This server gives an agent a cheap "is this safe?" call before it acts.
Install (MCP client)
{
"mcpServers": {
"agent-guard": { "command": "npx", "args": ["-y", "@liminallablibs/[email protected]"] }
}
}
Agent rule (paste into Cursor Rules / CLAUDE.md)
Before installing any npm/PyPI package, running install from a lockfile, installing a third-party skill/MCP plugin, or merging CI workflow changes, call the agent-guard MCP tools (
check_package,verify_lockfile,score_manifest,check_workflow). If verdict is SUSPICIOUS, REVIEW, or BLOCK/DANGER, stop and show findings to the user — do not proceed.
Quick demo
npx -y @liminallablibs/[email protected] # MCP clients spawn this via npx
# HTTP mirror:
npm run http && curl "http://localhost:8402/check?name=reactt&ecosystem=npm"
# → DANGER — typosquat of "react"
Or run directly:
npm install
node src/mcp-server.mjs # stdio MCP server
npm run http # optional HTTP mirror on :8402
HTTP endpoints (mirror of the MCP tools)
GET /check?name=<pkg>&ecosystem=<npm|pypi>POST /check-lockfile {lockfile_content, format}— format ∈ package-lock.json | yarn.lock | pnpm-lock.yaml | poetry.lock | requirements.txtPOST /score-manifest {manifest_type, manifest_content}POST /check-workflow {workflow_content}
License
MIT.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.