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Preferly

by ai.preferly
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Check llms.txt

check_llms_txt
Read-onlyIdempotent

Use this when the user asks whether a website has an llms.txt file or whether it's set up correctly. It fetches /llms.txt and reports whether it was found, its size and title, and every place it doesn't follow the llmstxt.org format (missing H1, no summary line, links that aren't Markdown or https), with line numbers. It also lists the homepage's main pages, which can seed a new llms.txt. Input: one public website. Reads robots.txt, the homepage and /llms.txt as PreferlyBot; changes nothing.

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": {
      "website": "<website>"
    },
    "name": "check_llms_txt"
  }
}

Input Schema

NameRequiredDescriptionDefault
websiteYesThe public website to check, like example.com or https://www.example.com. One site per call.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already flag read-only, idempotent, open-world, non-destructive, but the description adds substantive behavior beyond them: it reads robots.txt and the homepage in addition to /llms.txt, identifies itself as PreferlyBot, and enumerates exactly which violations are reported with line numbers. It also confirms 'changes nothing,' reinforcing the safety model with concrete detail.

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

Conciseness4/5

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

Front-loaded with the trigger condition, then the behavior, then the input and side-effect note — a sensible order. The long parenthetical of violation types is information-dense rather than padded, though slightly bulky for a single sentence.

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

Completeness5/5

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

Although there is no output schema, the description details the return contents (found status, size, title, format violations with line numbers, and homepage page list) and the side-effect profile. Nothing an agent needs in order to call this correctly and interpret results is missing.

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 parameter is fully documented in the schema, so the schema does the heavy lifting. The description only adds the constraint that the site must be public and that it is one site per call, which is marginal value over structured data.

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

Purpose5/5

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

States a specific verb and resource ('fetches /llms.txt and reports whether it was found...') plus the validation scope, which cleanly separates it from siblings like check_ai_crawler_access or generate_ai_robots_txt. An agent can identify what this returns without opening the schema.

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?

Opens with an explicit trigger: 'Use this when the user asks whether a website has an llms.txt file or whether it's set up correctly.' That is clear usage context, but there is no exclusion or routing language explaining when to prefer the robots.txt or crawler-access siblings instead.

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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