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

With no annotations provided, the description fully explains the tool's behavior: fetching the site and robots.txt, scoring multiple factors, and returning a score, grade, and specific fixes. It covers the core actions and outputs without ambiguity.

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

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

The description is three sentences, front-loaded with the main action, and contains no filler. Each sentence adds essential information: purpose, process, and output.

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

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

Given the moderate complexity, one required parameter, and no output schema, the description adequately explains the return value (score, grade, fix). It mentions all key components checked, making it complete for an agent to understand what the tool does.

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

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

The single parameter 'url' is fully described in the schema (100% coverage). The tool description repeats the same example format, adding no new semantics beyond what the schema already provides.

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

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

The description clearly states the tool's purpose: 'Check whether a website is visible to AI search engines' and details the specific checks (robots.txt, JSON-LD, etc.). It distinguishes from the sibling tool 'generate_ai_readiness_fixes' which focuses on providing fixes.

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

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

The description does not explicitly state when to use this tool versus the sibling or alternatives. While the action is clear, there is no guidance on context or prerequisites (e.g., website must be accessible).

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

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

No annotations are provided, so the description carries the full burden. It discloses that the tool generates starter fixes and that it uses the site's real title/description. However, it does not mention any potential side effects, permissions needed, or limitations beyond being a starter version. For a simple generative tool, this is adequate but not fully transparent.

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

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

The description is concise, consisting of two sentences that immediately convey the tool's purpose and outputs. Every sentence adds value, and the structure is front-loaded with the most important information.

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

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

For a tool with one parameter and no output schema, the description is fairly complete. It explains what outputs are generated and how to apply them. However, it lacks details on prerequisites, error handling, or a precise definition of 'AI search'. Overall, it covers the essential aspects.

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

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

Only one parameter (url) with schema description coverage at 100%. The description adds no additional meaning beyond the schema's description. Since schema coverage is high, baseline is 3. No enums or constraints, so no missing nuance.

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

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

The description clearly states the tool generates starter fixes for AI visibility, specifically Organization + FAQPage JSON-LD and an AI-friendly robots.txt. It also distinguishes from the sibling tool 'check_ai_readiness' which checks readiness, while this generates fixes.

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

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

Provides explicit instructions on how to use the outputs: paste JSON-LD into homepage <head> and replace robots.txt. Mentions it's a free starter version, implying usage context. Lacks explicit when-not-to-use or alternative scenarios, but the guidance is clear and actionable.

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