Rat Radar
Ask before you scrape: is a site open, may I fetch this URL, and how do I extract a page. No key.
Should I use this
Quality & Safety
Based on automated analysis of tool definitions and protocol compliance.
Context Cost
This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.
Install
One-Click Install
Add this to your `claude_desktop_config.json` file:
{
"mcpServers": {
"rat-radar": {
"url": "https://rat-radar.github-a18.workers.dev/mcp"
}
}
}Remote endpoints
https://rat-radar.github-a18.workers.dev/mcpstreamable-httpWhat it can do
Tool inventory
Tools (8)
🟢check_site(domain)
Can I scrape this site, and how? Returns a verdict (open, guarded, hostile, blocked), a hostility score, a recommended approach with steps and risks, bot protections, robots.txt and AI-crawler policy, machine-readable surfaces (sitemap, llms.txt, JSON-LD), and eu_ai_training: the signals the EDPB draft Guidelines 03/2026 on web scraping for generative AI count against collecting personal data (robots.txt, ai.txt, CAPTCHAs, login walls), as none, some or strong. Signals, not legal advice. Uses a weekly-probed dataset of several hundred sites; for any other domain it probes live from a datacenter IP.
Input Schema
{
"type": "object",
"properties": {
"domain": {
"type": "string",
"description": "A domain such as reddit.com"
}
},
"required": [
"domain"
]
}🟢get_playbook(domain, goal, platform)
What should I build with this site, and how? Give a domain and, optionally, a goal in plain words (for example "price alerts", "sales leads", "new jobs", "change log", "knowledge base"). Returns the best-matching workflow for that site: what it does, its cadence, a trigger, collect, shape, store, act pipeline, the fields to keep, and step-by-step build recipes for n8n, Make, Zapier and Apify (or just one with platform). Workflows marked found_on_this_site come from what the weekly probe saw on the site itself. Also returns the other workflows, the golden data, and the guardrails.
Input Schema
{
"type": "object",
"properties": {
"domain": {
"type": "string",
"description": "A domain such as amazon.com"
},
"goal": {
"type": "string",
"description": "optional, what you want to achieve"
},
"platform": {
"type": "string",
"enum": [
"n8n",
"make",
"zapier",
"apify"
],
"description": "optional, only return this platform (n8n also returns n8n_workflow, an importable file):'s recipe"
}
},
"required": [
"domain"
]
}🟢list_sites(category, verdict, limit)
List the sites in the dataset, optionally by category or verdict. Use it to find easy targets or to see what is covered.
Input Schema
{
"type": "object",
"properties": {
"category": {
"type": "string",
"description": "e.g. shop, jobs, news, property, travel, gov"
},
"verdict": {
"type": "string",
"enum": [
"open",
"guarded",
"hostile",
"blocked"
]
},
"limit": {
"type": "integer",
"description": "default 50, max 200"
}
}
}🟡xray_page(url, include_code)
How do I extract the data from THIS page? Fetches one public page and returns a ranked extraction plan: embedded JSON, JSON-LD, repeated records with CSS selectors and field names, tables, feeds, hidden API calls. Each source has a reliability, proof rows pulled from the real page, and the plan notes pagination and blockers. Set include_code for a starter Python script. Works on the raw HTML only, so JavaScript-built pages report a blocker instead of data. Use check_site first to learn whether the site allows scraping at all.
Input Schema
{
"type": "object",
"properties": {
"url": {
"type": "string",
"description": "Full page URL, e.g. https://example.com/products"
},
"include_code": {
"type": "boolean",
"description": "also return a starter Python script"
}
},
"required": [
"url"
]
}🟢xray_diagram(url, include_scene)
Draw a page's anatomy as an Excalidraw diagram: its regions, the ranked ways to get its data, proof rows, the best route and what the page is made of. Returns a download link for the .excalidraw file (give it to the user; it opens at excalidraw.com or in Obsidian) and a file_name. With include_scene, also the scene itself (a few hundred KB).
Input Schema
{
"type": "object",
"properties": {
"url": {
"type": "string",
"description": "Full page URL, e.g. https://example.com/products"
},
"include_scene": {
"type": "boolean",
"description": "also return the full Excalidraw scene JSON (large). Default false: just the download link"
}
},
"required": [
"url"
]
}🟢search_sites(query, limit)
Find sites in the dataset by a word: a name, host, category (jobs, shop, news, gov), country or verdict. Use it to discover what is covered before calling check_site.
Input Schema
{
"type": "object",
"properties": {
"query": {
"type": "string"
},
"limit": {
"type": "integer",
"description": "default 25, max 100"
}
},
"required": [
"query"
]
}🟢can_fetch(url, user_agent)
Does robots.txt allow fetching this exact URL? Fetches the live robots.txt and applies the longest-match rule for a user agent (default: * plus GPTBot, ClaudeBot, Google-Extended, CCBot, PerplexityBot). Returns allowed or not, the rule that decided it, and any crawl delay. A courtesy check, not a legal opinion.
Input Schema
{
"type": "object",
"properties": {
"url": {
"type": "string",
"description": "The full URL you want to fetch"
},
"user_agent": {
"type": "string",
"description": "optional, e.g. GPTBot"
}
},
"required": [
"url"
]
}⚪summary
Headline numbers across the dataset: verdict mix, how many sites block AI crawlers (and which), protection vendors, by category and country.
Input Schema
{
"type": "object",
"properties": {}
}Community
Evidence