io.github.assister-xyz/agenttrust

MCPcommunity
v0.1.2io.github.assister-xyzUnknownUpdated 5mo agoGitHub

Quality verification for AI agents and MCP servers. 6-axis scoring, adversarial probes.

Challenge-response quality verification for AI agents and MCP servers. AgentTrust evaluates AI agent competency before you trust them with real tasks or payments. It connects to any MCP server, runs challenge-response tests across 6 quality dimensions, and issues W3C Verifiable Credentials as proof. The AI agent ecosystem has identity (ERC-8004, SATI), post-hoc reputation (TARS, Amiko), and…

Automatically indexed from public sources. Not yet verified by the developer on Forge.Claim this listing →
5mo agoLast update
Package
Authorio.github.assister-xyz
LicenseUnknown
Version0.1.2
Sourcemcp-registry
Trust Status
B
60/100Good
Listed in Forge index+10/10
Publisher identity verified+0/20
Publisher: run `forge publish` from the package repo to claim ownership
Ed25519 publish signature+0/5
Included automatically when the publisher runs `forge publish`
Domain verification+0/5
Publisher: host /.well-known/forge.json on the package homepage with { "publisher": "<github-login>" }
npm Trusted Publishing (Sigstore)+0/5
Publish from GitHub Actions with --provenance so the attestation binds this package to this repo
npm maintainer match+0/5
Earned once your identity is verified above and that login is an npm maintainer of this package
CVE scan · clean+30/30
Static analysis · clean+20/20
Paste into Claude Code, Cursor, or any AI assistant to fix all gaps
StatusCommunity-indexed
PublisherUnverified
SignatureUnsigned
Domain
Provenance
DependenciesNot audited
Tool surface
Security scan✓ Cleanv0.1.2 · 2mo ago
EvalsNone
IndexedJun 13, 2026

Verification confirms publisher identity (repo ownership), not code safety. The security scan covers known CVEs and suspicious install scripts — it cannot prove the absence of malicious code.

About

Challenge-response quality verification for AI agents and MCP servers. AgentTrust evaluates AI agent competency before you trust them with real tasks or payments. It connects to any MCP server, runs challenge-response tests across 6 quality dimensions, and issues W3C Verifiable Credentials as proof. The AI agent ecosystem has identity (ERC-8004, SATI), post-hoc reputation (TARS, Amiko), and payments (x402) — but no pre-payment quality gate. AgentTrust fills this gap: verify competency first,…

Keywords
mcp