MeSquared Visibility

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 mentions comparing and highlighting results but does not disclose behavioral traits such as rate limits, authentication needs, response format, or potential side effects (e.g., whether it accesses external APIs). This leaves significant gaps for an AI agent to understand the tool's behavior.

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 a single, well-structured sentence that efficiently conveys the tool's purpose and outcome without unnecessary details. It is appropriately sized and front-loaded with key 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?

Given the complexity (comparison tool with no annotations and no output schema), the description is incomplete. It lacks details on what 'AI visibility scores' entail, how the comparison is performed, what the output looks like, or any behavioral context. This makes it inadequate for an AI agent to fully understand the tool's operation.

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?

Schema description coverage is 100%, so the schema already documents both parameters (first_url, second_url) with descriptions. The description adds no additional meaning beyond what the schema provides, such as URL format requirements or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.

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 specific action ('compare'), resource ('AI visibility scores of two business websites'), and outcome ('highlight which site is easier for AI systems to trust and recommend'). It distinguishes from the sibling tool 'scan_visibility' by focusing on comparison rather than scanning.

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 implies usage context (comparing two websites for AI visibility) but does not explicitly state when to use this tool versus the sibling 'scan_visibility' or any exclusions. It provides clear context but lacks explicit alternatives or when-not-to-use guidance.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the output (score 0-100, top issues) and the tool's purpose, but does not cover potential limitations such as rate limits, authentication needs, or error conditions. It adds some context (what the tool returns) but lacks details on operational constraints.

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 front-loaded with the core purpose in the first sentence, followed by details on the return values. Both sentences earn their place by providing essential information without redundancy, making it appropriately sized and efficient.

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 tool's moderate complexity (single parameter, no output schema), the description is mostly complete. It explains what the tool does and what it returns, but lacks details on behavioral aspects like error handling or performance. Without annotations or an output schema, it could benefit from more operational context.

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 schema description coverage is 100%, so the schema already documents the 'url' parameter. The description adds meaning by specifying it's a 'business website URL to scan', which provides context beyond the schema's generic description. However, it does not elaborate on URL format requirements or validation rules.

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 specific action ('Check how visible a business website is to AI search engines') and distinguishes it from the sibling tool 'compare_visibility' by focusing on a single URL scan rather than comparison. It specifies the target resources (AI search engines like ChatGPT, Perplexity, Google AI Overviews) and the outcome (score + issues).

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 implies when to use this tool (to assess AI visibility for a single business website) but does not explicitly state when not to use it or mention alternatives like the sibling 'compare_visibility'. It provides clear context (checking visibility to AI search engines) but lacks explicit exclusions or comparisons.

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