Security · MCP server
Commit — Supply Chain Risk Scoring: MCP server
Commit — Supply Chain Risk Scoring is an open-source MCP server that lets an AI agent score package supply chain risks from publisher, release and maintenance behavior. Its repository has 8 stars and its last commit is from October 6, 2026 and it is licensed under MIT.
What Commit — Supply Chain Risk Scoring does
According to its repository: “Supply chain risk scoring for npm, PyPI, Cargo and Go packages from behavioral signals: publisher depth, release consistency and maintenance patterns.”
An MCP server gives an AI agent extra tools it calls during the conversation: the agent reads their description, decides to use them and gets the result back. Commit — Supply Chain Risk Scoring is listed under Security, a category of 133 servers in the directory, where it ranks #100 by stars.
Topics declared by the repository: cli, dependencies, npm, pypi, security, supply-chain.
Installing and connecting Commit — Supply Chain Risk Scoring
Distribution modes declared in the official MCP registry:
- npm package (Node.js, usually started with
npx) - remote server (streamable-http): you connect to it by URL, with nothing to install locally
Like any MCP server, you declare it in your client's configuration (Claude Desktop, Claude Code, Cursor, VS Code, Codex…): a name, the command that starts it or its URL, and any environment variables. The client then starts it and lists its tools. Project language: TypeScript. Listed systems: macOS, Windows, Linux.
Security: what to check
An MCP server runs code with the rights you give it, and the model reads its tool descriptions: a malicious or compromised server can hijack the agent. Read the code, or pick a maintained and widely used server, before connecting it.
None of the risk signals we track (system access, secrets, missing licence, abandonment) applies to Commit — Supply Chain Risk Scoring. That does not replace reading the code.
Good practice: grant only the rights needed (least privilege), require human approval for irreversible actions, and run the AI agent security check.
Project activity and maintenance
Created on March 22, 2026, the piiiico/proof-of-commitment repository has 8 stars and 0 forks, with 0 open issues. It has more stars than 22% of the servers in the directory. It received changes this month. Version published in the registry: 1.36.0 (June 20, 2026).
Stars measure attention, not quality: a recent, heavily promoted project can collect them fast, while a useful but niche server will have few.
Verdict
Commit — Supply Chain Risk Scoring is a reasonable choice; check the points below before connecting it.
Strengths
- Active project: recent commit
- Listed in the official MCP registry
- Usable remotely, without installation
- Permissive licence (MIT)
Limitations
- Few visible users
Alternatives
- Semgrep17.2K ★
- World Monitor89.4K ★
- SafeDep Vet MCP1.1K ★
- Ida pro MCP13.1K ★
- GhidraMCP (LaurieWired)11.5K ★
- DashClaw313 ★
Learn more
Frequently asked questions
How do I install Commit — Supply Chain Risk Scoring?
Commit — Supply Chain Risk Scoring is distributed as npm package (Node.js, usually started with npx) and a remote server. Add it to your client's MCP configuration (Claude Desktop, Cursor, VS Code…) following the README of piiiico/proof-of-commitment.
Is Commit — Supply Chain Risk Scoring free?
The code is published under the MIT licence and can be used free of charge.
Is Commit — Supply Chain Risk Scoring safe?
No risk signal found (system access, secrets, licence, abandonment). As with any MCP server, keep rights minimal and read the code before sensitive use.
Is it an official server?
Nothing says so: it is a project published by piiiico on GitHub. Check who maintains it before giving it access.
What are the alternatives to Commit — Supply Chain Risk Scoring?
In the same category: Semgrep, World Monitor, SafeDep Vet MCP, and Ida pro MCP. Semgrep and World Monitor score higher on our grid.
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Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score