hlido-mcp
# Hlido MCP Server
[](https://smithery.ai/server/ankitkapur1992/hlido)
**Independent trust scores, claim audits, and comparisons for AI agents — queryable by your agent over [MCP](https://modelcontextprotocol.io).**
[Hlido](https://hlido.eu) 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:**
```bash
claude mcp add --transport http hlido https://hlido.eu/mcp
```
**Claude Desktop / Cursor / any MCP client** (`mcpServers` config):
```json
{
"mcpServers": {
"hlido": { "url": "https://hlido.eu/mcp" }
}
}
```
Also listed on [Smithery](https://smithery.ai/server/ankitkapur1992/hlido) 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 <need>" — 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](LICENSE)
- Review data: [CC-BY](https://github.com/ankitkapur1992-hlido/hlido-public) via the public data mirror and the [HF dataset](https://huggingface.co/datasets/hlido-eu/agent-benchmark)
## 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
```bash
docker build -t hlido-mcp .
docker run -p 8080:8080 hlido-mcp
```
The container runs the worker on the local [workerd](https://github.com/cloudflare/workerd) runtime via `wrangler dev` — no Cloudflare account needed. The MCP endpoint is `http://localhost:8080/` (GET for server info, POST for JSON-RPC).
TDQS
Scored across 14 tools
Every tool has a clearly defined and distinct purpose, with descriptions explicitly guiding when to use each. Even closely related tools like find_similar_agents, find_trusted, and recommend have well-delineated use cases (semantic search vs. keyword vs. structured constraints).
Most tools follow a lowercase verb_noun pattern (e.g., compare_agents, get_scorecard), but a few single-verb names like 'explain' and 'recommend' break the pattern. This is a minor deviation from full consistency.
With 14 tools covering discovery, evaluation, submission, and subscription, the count is well-scoped for the domain of agent trust. Each tool earns its place without feeling excessive or sparse.
The tool surface covers the core workflows (discover, evaluate, compare, report, request audits) comprehensively. A minor gap is the lack of a tool to list all reviewed agents, but the existing discovery tools effectively compensate.