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llms__check_llms_txt

[llms.txt 檢查工具]檢查一個網站對 AI 的友善程度:llms.txt 是否存在與格式問題、llms-full.txt、Markdown 頁面、robots.txt 對各 AI 爬蟲的設定、sitemap,回傳 0–100 分與改善建議。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes網站網址或網域

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses that the tool performs multiple checks and returns a 0–100 score with improvement suggestions, and the word 'check' implies a read-only operation. However, it does not state whether it accesses the live site, needs authentication, has rate limits, or whether results are cached.

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?

The description is a single dense sentence prefixed by a bracketed label, and it front-loads the core purpose. It contains no filler, though the enumeration of checks makes it slightly long for a one-input tool.

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 audit tool with full schema coverage and no annotations, the description covers the scope and the return shape (0–100 score plus suggestions). It is complete enough to call correctly, though it provides no detail about the output structure or how the score is composed.

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 description coverage is 100%, and the sole parameter is already documented as 'website URL or domain'. The description adds no further parameter meaning, which is acceptable given the high schema coverage, so the baseline of 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?

The description states a specific verb and resource: checking llms.txt and related AI-friendliness signals for a website. It clearly describes the scope, including llms.txt format, llms-full.txt, Markdown pages, robots.txt AI crawler settings, sitemap, and a score. It does not explicitly distinguish itself from sibling tools like validate_llms_txt or check_ai_crawler_access, so it stops short of a 5.

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

Usage Guidelines2/5

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

The description explains what the tool checks but offers no guidance on when to use it versus alternatives. Sibling tools such as llms__validate_llms_txt and llms__check_ai_crawler_access are not mentioned, and there are no exclusion conditions or recommended contexts.

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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