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AICompatible AI-readiness checker

Check AI readiness of a website

check_ai_readiness
Read-only

Scan a public website and return an AI-readiness score (0-100), sub-scores, AI-bot robots.txt access, and a list of issues with concrete fixes. Use when a user asks if a site is visible/optimized for ChatGPT, Perplexity, Claude, Gemini or AI search.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "url": "<url>"
    },
    "name": "check_ai_readiness"
  }
}

Input Schema

NameRequiredDescriptionDefault
urlYesDomain or URL to scan, e.g. example.com
categoryNoOptional site category; changes which schema types count as required.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and openWorldHint=true, so the agent knows this is a safe external read. The description helpfully discloses the return payload (scores, sub-scores, issues with fixes) in the absence of an output schema, and the word 'public' hints at reachability requirements. It says nothing about scan duration, rate limits, or how failures on unreachable or non-public sites are reported.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, zero waste, with the output contract front-loaded before the trigger condition. Each sentence carries distinct information for the agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description does the necessary work of enumerating return values, and the fully-covered two-parameter schema needs no further explanation. The only real gap is the unstated relationship to check_bot_access, which overlaps with the robots.txt access feature mentioned here.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so both parameters are already documented in the schema, including the category enum which 'changes which schema types count as required.' The description adds no further meaning about the url format or how category affects scoring, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a concrete verb ('Scan a public website') and enumerates the exact outputs: score 0-100, sub-scores, AI-bot robots.txt access, and issues with fixes. It does not explicitly distinguish itself from the sibling check_bot_access, even though robots.txt bot access is listed as one of its own outputs, so the boundary between the two is left for the agent to infer.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives a clear trigger: when a user asks whether a site is visible or optimized for ChatGPT, Perplexity, Claude, Gemini or AI search. There is no when-not guidance and no routing to the alternative check_bot_access for narrower robots.txt questions, but the intended context is unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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