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D

debelegger.nl

SCAN HISTORY

Result summary

Readiness profiles

Methodology legacy

Measured coverage

56/100

Methodology grade

C

50 checkpoints assessed

24

Passed

5

Partial

21

Failed

0

Skipped

Strongest category

Content & Semantics

Weakest category

Agent Protocols

Action plan

Priority actions Highest-impact implementation details
3 prioritized actions

Prioritized by exact impact on the overall stable-core score.

Failed Endpoint check timed out +4 overall pts

Request timed out - the site may be blocking automated access.

Failed WebMCP Declarative API +3 overall pts

No WebMCP signals found.

WebMCP Declarative API
HIGH

WebMCP is the W3C proposal for exposing website functionality directly to AI agents. Declarative tool annotations on forms let agents perform actions (search, purchase, book) without screen scraping.

Implement WebMCP to let AI agents interact with your site. Declarative: add tool-name and tool-description attributes to <form> elements. Imperative: use navigator.modelContext.provideContext() to register tools.

<form tool-name="search" tool-description="Search products by keyword">
  <input type="text" name="query" tool-param-description="Search query">
  <button type="submit">Search</button>
</form>
Failed A2A Agent Card +3 overall pts

No A2A Agent Card found at /.well-known/agent-card.json or /.well-known/agent.json.

A2A Agent Card
HIGH

Google's Agent-to-Agent (A2A) protocol enables autonomous agent-to-agent communication. An Agent Card at /.well-known/agent-card.json (new) or /.well-known/agent.json (legacy) advertises your agent's capabilities to other AI systems.

Create an A2A Agent Card at /.well-known/agent-card.json with required fields: name, description, url, version, capabilities, supportedInterfaces.

.well-known/agent.json json
{
  "name": "My Agent",
  "description": "An AI assistant for customer support",
  "url": "https://debelegger.nl/agent",
  "version": "1.0.0",
  "capabilities": {
    "streaming": true,
    "pushNotifications": false
  },
  "supportedInterfaces": [
    {
      "url": "https://debelegger.nl/mcp",
      "transport": "http",
      "protocol": "MCP/2025-06-18",
      "protocolBinding": "MCP",
      "protocolVersion": "2025-06-18"
    }
  ],
  "preferredTransport": "MCP",
  "skills": [
    {"id": "support", "name": "Customer Support"}
  ]
}
Fix with AI Skills
$ /plugin marketplace add bartwaardenburg/isagentready-skills

Detailed results

Agent Protocols Weight 15% Needs attention

9 failed checkpoints

0/100
AI Content Discovery Weight 30% Needs attention

4 failed, 1 partial

63/100
Security & Trust Weight 15% Needs attention

5 failed, 1 partial

49/100
Content & Semantics Weight 20% Partial progress

2 partial checkpoints

88/100
AI Search Signals Weight 20% Needs attention

3 failed, 1 partial

63/100
AI Training Exposure Supplemental

Does not affect the readiness score

No AI training protections detected. Your content may be freely used for AI model training.

Common Crawl presence Unknown

Could not determine the latest Common Crawl index.

Training crawler blocking Not found

No training crawlers are blocked in robots.txt. All known AI training bots can access your content.

TDMRep declaration Not found

No TDMRep file found at /.well-known/tdmrep.json. The TDM Reservation Protocol (W3C) lets you declare whether you reserve text and data mining rights. See w3.org/community/reports/tdmrep/CG-FINAL-tdmrep-20240510 for the specification.

ai.txt policy Not found

No ai.txt file found. ai.txt lets you declare AI usage preferences for your content.

Web Bot Auth Not found

No Web Bot Auth signature key directory found. Bots cannot prove their identity to origins via signed HTTP requests.

Developer Tools

Scan and fix from your editor

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  • Fix failing checks with AI agent skills
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SCAN
claude mcp add isagentready-mcp -- npx -y isagentready-mcp
FIX
/plugin marketplace add bartwaardenburg/isagentready-skills
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