Server Quality Checklist

Tool Scores

• check_robots_txtA
  3.6/5.0
  Behavior3/5

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

  With no annotations, the description carries full burden. It discloses that it fetches and parses per RFC 9309 and reports allowed/blocked crawlers plus sitemaps, but does not mention error handling (e.g., missing robots.txt), rate limiting, caching behavior, or whether it respects crawl-delay directives.

  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, each serving distinct purpose: first describes the core action and scope, second adds the sitemap feature. No filler words, and the most important information is front-loaded.

  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 simple fetch-and-parse tool with no output schema, the description provides enough context: it reports which AI crawlers are allowed/blocked and lists sitemaps. It lacks details on output format (e.g., JSON vs text) but is otherwise adequate for the tool's complexity.

  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 the schema already describes both parameters. The description adds no extra meaning beyond the schema (e.g., it does not clarify URL format or path default behavior beyond what the schema states). Baseline 3 is appropriate.

  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?

  The description clearly states the verb (fetch, parse, report), the resource (site's robots.txt), and the specific scope (AI crawler access). It explicitly lists AI crawlers and notes sitemaps, which distinguishes it from sibling tools that deal with LLMs.txt, schema markup, or meta directives.

  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 provides no guidance on when to use this tool versus the siblings (fetch_llms_txt, detect_schema_markup, check_meta_directives). It does not mention prerequisites, limitations, or when not to use it.

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

• fetch_llms_txtA
  3.6/5.0
  Behavior2/5

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

  With no annotations, the description only states what it checks and validates. It does not disclose return format, error handling, rate limits, or authentication needs, which are important for a query tool without output schema.

  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?

  Single sentence with clear action and scope. No fluff, front-loaded.

  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 simple tool with one parameter and no output schema, the description covers the core functionality. However, it could mention expected return structure or error cases for completeness.

  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?

  Only one parameter with full schema description coverage. The description does not add information beyond what the schema already states, meeting the baseline.

  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?

  The description clearly states the tool checks for the existence of /llms.txt and /llms-full.txt and validates structure against a spec, distinguishing it from sibling tools like check_robots_txt.

  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?

  No guidance on when to use versus alternatives or when not to use. The context is implied but not explicit.

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

• check_meta_directivesA
  3.8/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries full burden. It describes what the tool reports but does not disclose behavioral traits like read-only nature, error handling, or authentication requirements. It is adequate but lacks depth for a standalone description.

  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?

  The description is a single, well-structured sentence that is front-loaded with the core action ('report indexing directives') and includes specific details. Every word earns its place, with no redundancy.

  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 simple one-parameter tool with no output schema, the description covers the main functionality. It could mention what happens on error or hint at output format, but overall it is reasonably complete for the tool's complexity.

  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% for the single 'url' parameter, which already documents its type and format. The tool description adds no additional meaning beyond the schema, so the baseline score of 3 applies.

  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?

  The description clearly states the tool's purpose: reporting indexing directives from meta robots tags and X-Robots-Tag headers. It specifically mentions bot-specific and noai/noimageai tags, distinguishing it from siblings like check_robots_txt (site-level), fetch_llms_txt, and detect_schema_markup.

  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?

  The description implies usage for checking page-level meta directives but provides no explicit when-to-use or when-not-to-use guidance. It does not mention alternatives or exclude scenarios, leaving the agent to infer from siblings.

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

• detect_schema_markupA
  4/5.0
  Behavior4/5

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

  Without any annotations, the description effectively communicates the tool's behavior: extracting JSON-LD, inventorying types, flagging important ones, and performing sameAs disambiguation. It is transparent about its functionality, though it doesn't mention edge cases or error handling.

  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?

  The description consists of two concise sentences. The first sentence covers the core function, and the second adds important specifics about flagged types and disambiguation. No extraneous 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?

  Given no output schema, the description explains that the tool extracts JSON-LD and provides an inventory of types with flags. This covers the main output expectation. However, it could be more explicit about the return format or what happens when no JSON-LD is found. Still, it's sufficiently complete for a single-parameter tool without annotations.

  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?

  The schema provides 100% coverage for the single parameter 'url' with a clear description. The tool description adds context that the URL points to a page containing JSON-LD, but this is inherent from the tool's purpose. No additional parameter semantics beyond the schema are needed.

  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?

  The description clearly states the tool extracts JSON-LD structural data and inventories schema.org types, specifically naming the relevant types for AI discoverability. This distinguishes it from sibling tools like check_robots_txt or fetch_llms_txt which handle other SEO aspects.

  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?

  The description implies the tool is used to inspect schema markup on a page but does not explicitly state when to use it versus alternatives. No exclusion criteria or specific context is provided to guide tool selection among siblings.

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