PureRank MCP Server
Enables AI agents to score drafts for AI-content-spam risk before publishing and scan entire sites, providing a pre-publish QA gate with pass/warn/fail verdicts.
README
PureRank MCP server
A small Model Context Protocol server that gives your AI agent a pre-publish QA gate and site-level AI-content-spam scoring, backed by PureRank.
If your pipeline drafts and publishes content with an LLM, wire this in so the agent scores every draft before it publishes — and only ships content that reads as human-edited, not scaled AI slop of the kind Google's spam updates target.
- Free and open-source. The connector is free; it uses PureRank's public API.
- Free quota, no crawl needed for drafts. A free PureRank account includes an API key with a daily allowance for the draft gate (25 draft scores/day) plus a few full site scans/day. That is enough to gate a real publishing cadence. Higher volume is on the paid plans.
- No secrets in code. Configured entirely with environment variables.
Quick start
1. Get an API key — create a free account at https://pure-rank.com, open the dashboard, and create an API key (the free plan includes one). Keep it secret.
2. Add the server to Claude Code (requires Node.js ≥ 18):
claude mcp add purerank --env PURERANK_API_KEY=YOUR_KEY -- npx -y purerank-mcp-server
Or add it to any MCP client config (.mcp.json / claude_desktop_config.json):
{
"mcpServers": {
"purerank": {
"command": "npx",
"args": ["-y", "purerank-mcp-server"],
"env": { "PURERANK_API_KEY": "YOUR_KEY_HERE" }
}
}
}
3. Use it. Ask your agent things like "score this draft with PureRank before publishing", or make it a standing rule: draft → purerank_score_draft → publish only if gate == "pass", otherwise revise and re-score.
Tools
| Tool | What it does |
|---|---|
purerank_score_draft |
Score ONE draft (text or HTML) for AI-content-spam risk without crawling. Returns a 0–100 score, a pass/warn/fail gate, a per-signal breakdown, and human-readable findings. This is the pre-publish gate. |
purerank_scan_site |
Crawl and score a whole site. Submits the scan, polls to completion, returns the score, top risk signals, and a shareable report link. |
purerank_get_report |
Fetch the latest report summary for a domain (or a report id) without re-scanning. |
purerank_score_draft
The pre-publish gate. Content-only — nothing is fetched, so it is fast and cheap.
Input: { text? , html? , title? , url? , response_format? } — provide text (plain/markdown) or html.
Output (JSON):
{
"score": 82,
"gate": "fail",
"verdict": "Likely AI-generated",
"confidence": "high",
"words": 512,
"language": "en",
"signals": { "lexical": 100, "burstiness": 78, "vocabulary": 40, "repetition": 5, "formatting": 12 },
"explanations": ["39.5 AI stock phrases /1k words", "uniform sentence rhythm (CV 0.31)"],
"embedding": { "available": true, "margin": 0.34, "adjustment": 12 }
}
Gate bands: pass (<30, publish-ready) · warn (30–54, review) · fail (≥55, revise).
purerank_scan_site
Input: { url , max_pages? , wait_seconds? , response_format? }
Output (JSON): { domain, score, verdict, confidence, profile, pages_analyzed, top_signals[], report_url, report_id, cached }. Established global brands return { not_applicable: true, note } and are not scored.
purerank_get_report
Input: { query , response_format? } — query is a domain, a report id, or a /r/{id} URL.
Output: same summary shape as purerank_scan_site (domain lookups return the latest public report).
All tools accept response_format: "markdown" (default, human-readable) or "json" (machine-readable). Machine-readable data is also always returned as structuredContent.
Configuration (environment variables)
| Variable | Required | Default | Description |
|---|---|---|---|
PURERANK_API_KEY |
yes | — | Your PureRank API key. |
PURERANK_API_URL |
no | https://pure-rank.com |
API base URL. Override to point at a local or self-hosted instance. |
PURERANK_SCAN_WAIT_S |
no | 180 |
Default seconds to wait for a site scan before returning a still-running status. |
PURERANK_TIMEOUT_MS |
no | 30000 |
Per-request network timeout. |
Running from source
git clone https://github.com/vadimsv1/purerank-mcp-server.git
cd purerank-mcp-server
npm install
npm run build
claude mcp add purerank --env PURERANK_API_KEY=YOUR_KEY -- node /absolute/path/to/purerank-mcp-server/dist/index.js
Inspect it with the MCP Inspector:
npx @modelcontextprotocol/inspector node dist/index.js
Development: npm run dev (tsx watch) · npm run build (tsc → dist/) · npm start.
Notes
- stdio transport: logs go to stderr; stdout is reserved for the MCP protocol.
- The score is an explainable heuristic from public signals — not a Google metric and not proof of authorship. Use it to prioritize human review.
- API reference: https://pure-rank.com/api/docs.
License
MIT © SVS Project LLC
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.