Landing page analysis

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

The annotations already declare the tool as read-only (readOnlyHint=true) and non-destructive (destructiveHint=false), which covers the primary behavioral aspect. The description adds context about data sources and analysis, but does not mention any additional behavioral constraints (e.g., rate limits, authentication, or side effects like caching). Since the annotation covers the safety profile, the score is moderate—the description adds some value but not significant behavioral detail.

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 and well-structured, with two clear sentences outlining the core analysis and a brief note on date format. It avoids unnecessary jargon or fluff, keeping the essential information front-loaded. The structure is clean and easy to parse, making it highly effective for quick comprehension.

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?

While the description provides a solid overview of the tool's function and output, it does not fully explain the meaning of all parameters (customer_id, property_id) or the expected output structure. However, since an output schema exists, the return values are implicitly documented. The missing parameter details leave the context incomplete, making it average for a tool with this complexity.

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 provides no descriptions for the four parameters, and the description only clarifies date formatting for date_range_start/end. The customer_id and property_id parameters are left completely unexplained, leaving the agent to guess their meaning or purpose. Given the schema coverage is 0%, the description must compensate, but it only partially does so for dates. This results in low parameter semantics.

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: to analyze landing page conversion performance by combining ad final URLs with GA4 data. It also highlights specific outputs (sessions, conversion rates, bounce rates) and distinct features (detecting zero-conversion pages and orphaned URLs), which effectively differentiates it from sibling tools like run_ga4_report or get_campaign_performance.

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 explains what the tool does but does not provide explicit guidance on when to use it relative to alternatives. For example, it doesn't state that this should be used when a cross-channel landing page analysis is needed versus a standard GA4 report. The absence of any comparative or conditional guidance leaves the agent to infer usage context from the description alone.

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