agentready
Server Details
Can ChatGPT, Claude and Perplexity reach and read a website? Score, problems found and fixes.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-11-25
- URL
TDQS
Score is being calculated.
Available Tools
1 toolcheck_websiteAInspect
Check whether AI agents (ChatGPT, Claude, Perplexity) can reach and read a website: robots.txt rules per agent, firewall blocking, content without JavaScript, structured data. Returns a 0-100 score, the problems found, and how to fix them.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Website address, for example example.com |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does well: it names the concrete checks performed (robots.txt per agent, firewall blocking, JavaScript-free rendering, structured data) and the return shape (0-100 score, problems found, fix guidance). It does not disclose whether checks are live-fetched, latency, or rate limits, so it is not exhaustive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with what is checked and followed by the output contract. Every clause carries information; there is no padding or restatement of the tool name.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter, no-output-schema tool, the description covers the required ground: what is inspected, the result format (score, problems, fixes), and the scope of agents considered. Nothing needed to invoke or interpret the tool correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There is a single parameter with 100% schema description coverage ('Website address, for example example.com'), so the schema already documents it fully. The description adds no format or protocol details beyond the schema, which is the expected baseline when coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Check') plus a well-scoped resource (whether AI agents can reach and read a website) and then enumerates the exact facets checked: robots.txt rules per agent, firewall blocking, JS-free content, structured data. An agent knows precisely what this tool does and what it is not (it is not a general site health or SEO checker).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There are no sibling tools, so routing guidance is unnecessary, and the description makes the use context clear: verifying AI-agent crawlability/accesibility for a given URL. It does not state exclusions (e.g., 'do not use for non-public sites' or 'requires the site to be reachable'), so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
- First observed
check_website
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Related MCP Servers
- AlicenseAqualityCmaintenanceCheck whether a website is visible to AI search engines (ChatGPT, Perplexity, Claude, Google AI Overviews). Returns a 0-100 readiness score, a grade, and a specific fix for each gap. Dependency-free, no API keys.28 npmMIT
- AlicenseNot gradedqualityBmaintenanceA remote Model Context Protocol server developed by Inxy.ai that lets any AI agent — Claude, ChatGPT, Cursor, and others — audit a website or Shopify store for AEO / GEO / LLMO readiness: how likely ChatGPT, Claude, Perplexity, and Google AI are to cite it.MIT
- AlicenseAqualityDmaintenanceAudits AI-bot visibility: robots.txt per-bot for 22 AI user-agents (GPTBot/ClaudeBot/PerplexityBot/etc), Cloudflare flags, JSON-LD, sitemap, llms.txt, SPA shell, plus cross-model brand mentions via Perplexity + OpenRouter. 0-100 score. SSRF-guarded, spend-capped.41MIT
- AlicenseAqualityCmaintenanceChecks a website's robots.txt and Cloudflare settings to identify AI crawler blocking. Also generates llms.txt content to improve visibility to AI answer engines.334 npmMIT
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