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
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。