Server data from the Official MCP Registry
Independent AI-agent reviews: trust checks, evidence scorecards, incident registry, recommendations.
About
Independent AI-agent reviews: trust checks, evidence scorecards, incident registry, recommendations.
Remote endpoints: streamable-http: https://hlido.eu/mcp
Security Report
A well-structured MCP server for querying Hlido's public AI-agent review registry. Authentication is intentionally absent (public data only), permissions are appropriate for a read-heavy data adapter, and the code follows secure patterns for handling credentials and external data. Minor findings relate to code quality and telemetry edge cases rather than security vulnerabilities. Supply chain analysis found 1 known vulnerability in dependencies (0 critical, 1 high severity).
3 files analyzed · 8 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
How to Install & Connect
Available as Local & Remote
This plugin can run on your machine or connect to a hosted endpoint. during install.
Documentation
View on GitHubFrom the project's GitHub README.
Hlido MCP Server
Independent trust scores, claim audits, and comparisons for AI agents — queryable by your agent over MCP.
Hlido is an independent AI-agent review platform ("Rotten Tomatoes for AI agents"). We test agents hands-on and publish evidence-backed scorecards: a 0–100 score, tier verdict, per-claim PASS/FAIL audit, and signed screenshots. This repository contains the source of the MCP server that exposes that registry to other agents.
Use the hosted server (no install)
The server runs as a Cloudflare Worker at:
https://hlido.eu/mcp
Claude Code:
claude mcp add --transport http hlido https://hlido.eu/mcp
Claude Desktop / Cursor / any MCP client (mcpServers config):
{
"mcpServers": {
"hlido": { "url": "https://hlido.eu/mcp" }
}
}
Also listed on Smithery and mcp.so.
Tools
| Tool | What it answers |
|---|---|
trust_check | "Is agent X trustworthy?" — score, tier, verdict for a slug |
find_trusted | "Find me a trusted agent for " — filtered registry search |
verify_claim | "Does X really do Y?" — per-claim PASS/FAIL evidence |
compare_agents | Side-by-side scorecard comparison |
get_scorecard | Full sanitized scorecard JSON for a slug |
find_similar_agents | Semantic nearest neighbours to a given agent |
submit_agent | Nominate an agent for review |
report_review_issue | Flag a problem with a published review |
request_quick_audit | Ask for a fast re-check of a stale review |
(plus discovery/metadata tools — see src/index.mjs for the live tool table)
Design principles
- Public data only. The server reads the same JSON published at
hlido.eu/data/*(registry, scorecards, attestations). It never exposes scoring weights, grader assertions, or editorial drafts — the methodology stays private; the outcomes and evidence are public. - No auth, no tracking. Anonymous JSON-RPC. Lightweight daily per-tool counters are the only telemetry.
- Thin by intent. This is an adapter over open data. The review pipeline, testing engine, and scoring model live elsewhere and are not part of this repository.
Self-hosting
It's a standard Cloudflare Worker. Copy wrangler.toml.example to wrangler.toml, set your account id, and npx wrangler deploy. Optional bindings (KV cache, Vectorize similarity index) degrade gracefully when absent — the worker falls back to fetching the public JSON directly.
Data & licensing
- Code: Apache-2.0
- Review data: CC-BY via the public data mirror and the HF dataset
Links
- Website: https://hlido.eu
- Agent manifest: https://hlido.eu/agent-manifest.json
- llms.txt: https://hlido.eu/llms.txt
- Public data mirror: https://github.com/ankitkapur1992-hlido/hlido-public
Run with Docker
docker build -t hlido-mcp .
docker run -p 8080:8080 hlido-mcp
The container runs the worker on the local workerd runtime via wrangler dev — no Cloudflare account needed. The MCP endpoint is http://localhost:8080/ (GET for server info, POST for JSON-RPC).
Reviews
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