Agent Tool Finder

Check whether AI agents can use a website

check_agent_readiness

Free check of whether AI agents can use a business's website: can they get in, understand it, act on it (API, MCP) and trust it. Returns a 0–100 score, a verdict, and each check with what was found and how to fix it.

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_agent_readiness"
  }
}

Input Schema

Name	Required	Description	Default
`url`	Yes	The website to check, e.g. example.com or https://www.example.com

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 1, 2026

TDQS

A3.6/5.0

Behavior3/5

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

No annotations exist, so the description carries the full burden. It does disclose the return shape (0–100 score, verdict, per-check findings and remediation) and that the check is free, which is genuinely useful. It says nothing about whether the site is fetched live, latency, rate limits, or failure behavior for unreachable URLs — meaningful gaps for a network-facing check.

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?

One sentence covering scope plus one covering output, with the purpose front-loaded and zero filler. Every clause conveys new information.

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?

For a one-parameter tool with no output schema, the description covers both what is checked and what comes back, which is the right allocation of effort. The remaining gap is operational context (live fetch, errors, timing) rather than anything needed to form the call.

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% and the single url parameter is already documented with examples (example.com or https://www.example.com). The description adds no syntax, normalization, or scope constraints beyond that, so the schema does all the work and 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 specific verb and resource: a check of whether AI agents can use a website, with the four dimensions (access, understanding, action via API/MCP, trust) spelled out. That is far more specific than the title alone. It does not, however, distinguish itself from the sibling check_payability, which shares the 'check_' framing and could be confused with it.

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

Usage Guidelines3/5

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

Usage is implied by the object of the check (a business's website) and the word 'Free' signals no cost barrier, but there is no explicit when-to-use, when-not-to-use, or routing to check_payability / find_tools. The agent must infer that this is the entry-point audit rather than a narrower sibling check.

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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